{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mosobay/Dropbox (MIT)/research/csop2/dataverse/csop_helper.py:9: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  task_specs = yaml.load(file.read())\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns \n",
    "import re \n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import csop2_helper as csop2\n",
    "import csop_helper as csop\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "from scipy.stats import entropy\n",
    "import scipy\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.linear_model import LinearRegression, ElasticNet\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from tqdm import tqdm\n",
    "from copy import deepcopy\n",
    "\n",
    "from importlib import reload  \n",
    "csop2 = reload(csop2)\n",
    "\n",
    "import h2o \n",
    "from h2o.automl import H2OAutoML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true"
   },
   "source": [
    "# Reading data & defining plotting utilities \n",
    "* Figures use the output of `in_sample_preprocessing.ipynb`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle(\"phase_2_within_sample_processed.pkl\")\n",
    "\n",
    "df[\"relativegain_score_zscore\"] = df[\"score_zscore\"] - df[\"p1_score_solozscore_max\"]\n",
    "df[\"relativegain_round_duration_zscore\"] = df[\"round_duration_zscore\"] - df[\"p1_duration_solozscore_max\"]\n",
    "df[\"relativegain_efficiency_zscore\"] = df[\"efficiency_zscore\"] - df[\"p1_efficiency_solozscore_max\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "tick_size = 20\n",
    "label_size = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_marker_alpha(axes: list, marker_indices: list, alpha: float):\n",
    "    for axis in axes:\n",
    "        for marker in marker_indices:\n",
    "            plt.setp(axis.collections[marker], alpha=alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bonferroni correction vars \n",
    "n_corrections = (5 #unregistered comparisons \n",
    "                 * 3 #outcomes\n",
    "                 * 5 #complexities\n",
    "                 + 2 #behaviors \n",
    "                 * (4 + 4) # 4 complexity and 4 round index params \n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true"
   },
   "source": [
    "# Figure 2: Group composition and group performance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dfs = []\n",
    "\n",
    "for outcome in [\"score_zscore\", \"round_duration_zscore\", \"efficiency_zscore\"]:\n",
    "    for complexity in csop2.COMPLEXITIES:\n",
    "        model = smf.ols(\"{} ~ p1_score_mean_zscore + p1_score_std_zscore + rme_mean_zscore + cogstyle_diversity_q3\".format(outcome), \n",
    "                       data = df.query(\"complexity == '{}'\".format(complexity))).fit()\n",
    "\n",
    "        model_dfs.append(pd.DataFrame({'coef':model.params, 'se':model.bse, 'outcome':outcome, 'complexity':complexity}).reset_index().rename(columns={'index':'independent_variable'}))\n",
    "        \n",
    "    model = smf.mixedlm(\"{} ~ (p1_score_mean_zscore + p1_score_std_zscore + rme_mean_zscore + cogstyle_diversity_q3)\".format(outcome), \n",
    "                   data = df, groups=\"game_id\").fit()\n",
    "\n",
    "    model_dfs.append(pd.DataFrame({'coef':model.params, 'se':model.bse, 'outcome':outcome, 'complexity':\"Overall\"}).reset_index().rename(columns={'index':'independent_variable'}))\n",
    "    \n",
    "    \n",
    "    \n",
    "df_regressions = pd.concat(model_dfs,ignore_index=True)\n",
    "df_regressions['err'] = df_regressions['se']*1.96"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outcomes = [\"score_zscore\", \"round_duration_zscore\", \"efficiency_zscore\"]\n",
    "ivs = [\"p1_score_mean_zscore\", \"p1_score_std_zscore\", \"rme_mean_zscore\",\"cogstyle_diversity_q3[T.True]\"]\n",
    "\n",
    "fig, axes= csop2.composition_vs_perf_plots(df_regressions,\n",
    "                                          outcomes=outcomes,\n",
    "                                          variable_order=ivs, figsize=(13,7),\n",
    "                                          ind_var_labels=[\"Skill\", \"Skill\\ndiversity\", \"Social\\nperceptiveness\",\"Cognitive style\\ndiversity\"],\n",
    "                                          erroralpha=0.5)\n",
    "\n",
    "\n",
    "axes[0].set_xlabel(\"\")\n",
    "axes[1].set_xlabel(\"Coefficient\", fontsize=25)\n",
    "axes[2].set_xlabel(\"\")\n",
    "\n",
    "axes[0].set_title(\"Normalized score\", fontsize=25)\n",
    "axes[1].set_title(\"Duration\", fontsize=25)\n",
    "axes[2].set_title(\"Efficiency\", fontsize=25)\n",
    "\n",
    "axes[0].tick_params(axis=\"y\", rotation=30)\n",
    "\n",
    "\n",
    "plot_marker_alpha(axes, [0,1,2,3,4], 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true"
   },
   "source": [
    "# Figure 3: Out-of-sample (OOS) prediction of phase 2 performance using a range of models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: java version \"18\" 2022-03-22; Java(TM) SE Runtime Environment (build 18+36-2087); Java HotSpot(TM) 64-Bit Server VM (build 18+36-2087, mixed mode, sharing)\n",
      "  Starting server from /Users/mosobay/opt/anaconda3/envs/py38/lib/python3.8/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /var/folders/ht/v2f_4cfn32dfqkbkq67phfth0000gp/T/tmpe8wrdlsk\n",
      "  JVM stdout: /var/folders/ht/v2f_4cfn32dfqkbkq67phfth0000gp/T/tmpe8wrdlsk/h2o_mosobay_started_from_python.out\n",
      "  JVM stderr: /var/folders/ht/v2f_4cfn32dfqkbkq67phfth0000gp/T/tmpe8wrdlsk/h2o_mosobay_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n",
      "Warning: Your H2O cluster version is too old (11 months)!Please download and install the latest version from http://h2o.ai/download/\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>08 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>America/New_York</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.36.1.1</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>11 months !!!</td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_mosobay_0edwjl</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>4 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.8.11 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ------------------------------\n",
       "H2O_cluster_uptime:         08 secs\n",
       "H2O_cluster_timezone:       America/New_York\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.36.1.1\n",
       "H2O_cluster_version_age:    11 months !!!\n",
       "H2O_cluster_name:           H2O_from_python_mosobay_0edwjl\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    4 Gb\n",
       "H2O_cluster_total_cores:    8\n",
       "H2O_cluster_allowed_cores:  8\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://127.0.0.1:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.8.11 final\n",
       "--------------------------  ------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "AutoML progress: |\n",
      "11:20:20.805: Project: AutoML_1_20230314_112020\n",
      "11:20:20.807: Setting stopping tolerance adaptively based on the training frame: 0.05\n",
      "11:20:20.807: Build control seed: 2022\n",
      "11:20:20.808: training frame: Frame key: AutoML_1_20230314_112020_training_Key_Frame__upload_89f176e6f81b418d20d1a7637957473d.hex    cols: 42    rows: 196  chunks: 1    size: 67449  checksum: 3391180774570991272\n",
      "11:20:20.808: validation frame: NULL\n",
      "11:20:20.809: leaderboard frame: NULL\n",
      "11:20:20.809: blending frame: NULL\n",
      "11:20:20.809: response column: p2_cumulative_normscore\n",
      "11:20:20.809: fold column: null\n",
      "11:20:20.809: weights column: null\n",
      "11:20:20.826: Loading execution steps: [{XGBoost : [def_2 (1g, 10w), def_1 (2g, 10w), def_3 (3g, 10w), grid_1 (4g, 90w), lr_search (7g, 30w)]}, {GLM : [def_1 (1g, 10w)]}, {DRF : [def_1 (2g, 10w), XRT (3g, 10w)]}, {GBM : [def_5 (1g, 10w), def_2 (2g, 10w), def_3 (2g, 10w), def_4 (2g, 10w), def_1 (3g, 10w), grid_1 (4g, 60w), lr_annealing (7g, 10w)]}, {DeepLearning : [def_1 (3g, 10w), grid_1 (4g, 30w), grid_2 (5g, 30w), grid_3 (5g, 30w)]}, {completion : [resume_best_grids (6g, 60w)]}, {StackedEnsemble : [monotonic (9g, 10w), best_of_family_xglm (10g, 10w), all_xglm (10g, 10w)]}]\n",
      "11:20:20.850: Disabling Algo: DeepLearning as requested by the user.\n",
      "11:20:20.850: Disabling Algo: XGBoost as requested by the user.\n",
      "11:20:20.851: AutoML job created: 2023.03.14 11:20:20.764\n",
      "11:20:20.852: AutoML build started: 2023.03.14 11:20:20.852\n",
      "11:20:20.867: AutoML: starting GLM_1_AutoML_1_20230314_112020 model training\n",
      "\n",
      "██\n",
      "11:20:24.51: New leader: GLM_1_AutoML_1_20230314_112020, rmse: 24.94806167228948\n",
      "11:20:24.56: AutoML: starting GBM_1_AutoML_1_20230314_112020 model training\n",
      "11:20:24.57: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 196.0.\n",
      "11:20:24.59: AutoML: starting DRF_1_AutoML_1_20230314_112020 model training\n",
      "\n",
      "█████\n",
      "11:20:32.944: AutoML: starting GBM_2_AutoML_1_20230314_112020 model training\n",
      "\n",
      "█\n",
      "11:20:38.213: New leader: GBM_2_AutoML_1_20230314_112020, rmse: 23.478990684781774\n",
      "11:20:38.214: AutoML: starting GBM_3_AutoML_1_20230314_112020 model training\n",
      "\n",
      "███\n",
      "11:20:44.130: AutoML: starting GBM_4_AutoML_1_20230314_112020 model training\n",
      "\n",
      "██\n",
      "11:20:49.440: AutoML: starting XRT_1_AutoML_1_20230314_112020 model training\n",
      "\n",
      "████\n",
      "11:21:02.121: AutoML: starting GBM_5_AutoML_1_20230314_112020 model training\n",
      "\n",
      "██\n",
      "11:21:07.841: AutoML: starting GBM_grid_1_AutoML_1_20230314_112020 hyperparameter search\n",
      "\n",
      "█████████████████████\n",
      "11:21:10.862: New leader: GBM_grid_1_AutoML_1_20230314_112020_model_1, rmse: 22.731363970742002\n",
      "11:21:22.907: New leader: GBM_grid_1_AutoML_1_20230314_112020_model_4, rmse: 22.372654912991706\n",
      "11:24:27.732: AutoML: starting GBM_grid_1_AutoML_1_20230314_112020 hyperparameter search\n",
      "\n",
      "██████████████████\n",
      "11:28:18.50: Retraining best GBM with learning rate annealing: GBM_grid_1_AutoML_1_20230314_112020_model_4\n",
      "11:28:18.50: AutoML: starting GBM_lr_annealing_selection_AutoML_1_20230314_112020_select_model model training\n",
      "\n",
      "█████| (done) 100%\n",
      "\n",
      "11:28:20.997: No base models, due to timeouts or the exclude_algos option. Skipping StackedEnsemble 'monotonic'.\n",
      "11:28:21.11: AutoML: starting StackedEnsemble_BestOfFamily_1_AutoML_1_20230314_112020 model training\n",
      "11:28:21.155: StackedEnsemble_BestOfFamily_1_AutoML_1_20230314_112020 [StackedEnsemble best_of_family_xglm (built with AUTO metalearner, using top model from each algorithm type)] failed: java.lang.RuntimeException: water.exceptions.H2OIllegalArgumentException: Not enough data to create 196 random cross-validation splits. Either reduce nfolds, specify a larger dataset (or specify another random number seed, if applicable).\n",
      "11:28:21.159: AutoML: starting StackedEnsemble_AllModels_1_AutoML_1_20230314_112020 model training\n",
      "11:28:21.288: StackedEnsemble_AllModels_1_AutoML_1_20230314_112020 [StackedEnsemble all_xglm (built with AUTO metalearner, using all AutoML models)] failed: java.lang.RuntimeException: water.exceptions.H2OIllegalArgumentException: Not enough data to create 196 random cross-validation splits. Either reduce nfolds, specify a larger dataset (or specify another random number seed, if applicable).\n",
      "11:28:21.291: Actual modeling steps: [{GLM : [def_1 (1g, 10w)]}, {DRF : [def_1 (2g, 10w)]}, {GBM : [def_2 (2g, 10w), def_3 (2g, 10w), def_4 (2g, 10w)]}, {DRF : [XRT (3g, 10w)]}, {GBM : [def_1 (3g, 10w), grid_1 (4g, 60w)]}, {completion : [resume_best_grids (6g, 60w)]}, {GBM : [lr_annealing (7g, 10w)]}]\n",
      "11:28:21.291: AutoML build stopped: 2023.03.14 11:28:21.291\n",
      "11:28:21.291: AutoML build done: built 94 models\n",
      "11:28:21.291: AutoML duration:  8 min  0.439 sec\n",
      "\n",
      "Model Details\n",
      "=============\n",
      "H2OGradientBoostingEstimator :  Gradient Boosting Machine\n",
      "Model Key:  GBM_grid_1_AutoML_1_20230314_112020_model_4\n",
      "\n",
      "\n",
      "Model Summary: \n"
     ]
    },
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       "     number_of_trees  number_of_internal_trees  model_size_in_bytes  \\\n",
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      "\n",
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 392.8066253766598\n",
      "RMSE: 19.819349771792712\n",
      "MAE: 14.60288581069635\n",
      "RMSLE: 0.04694593464586726\n",
      "Mean Residual Deviance: 392.8066253766598\n",
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on cross-validation data. **\n",
      "\n",
      "MSE: 500.5356878558119\n",
      "RMSE: 22.372654912991706\n",
      "MAE: 15.892864083232437\n",
      "RMSLE: 0.05257096666204033\n",
      "Mean Residual Deviance: 500.5356878558119\n",
      "\n",
      "Cross-Validation Metrics Summary: \n"
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       "      <td>mean_residual_deviance</td>\n",
       "      <td>563.621700</td>\n",
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       "      <td>66.644930</td>\n",
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       "      <td>mse</td>\n",
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       "      <td>87.045720</td>\n",
       "      <td>66.644930</td>\n",
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       "      <td>residual_deviance</td>\n",
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       "      <th>5</th>\n",
       "      <td>rmse</td>\n",
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       "      <th>6</th>\n",
       "      <td>rmsle</td>\n",
       "      <td>0.039509</td>\n",
       "      <td>0.039005</td>\n",
       "      <td>0.019392</td>\n",
       "      <td>0.017479</td>\n",
       "      <td>0.012361</td>\n",
       "      <td>0.039438</td>\n",
       "      <td>0.012292</td>\n",
       "      <td>0.029666</td>\n",
       "      <td>0.022220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086741</td>\n",
       "      <td>0.026442</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.047909</td>\n",
       "      <td>0.082595</td>\n",
       "      <td>0.008868</td>\n",
       "      <td>0.040064</td>\n",
       "      <td>0.034965</td>\n",
       "      <td>0.073204</td>\n",
       "      <td>0.019685</td>\n",
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       "</table>\n",
       "<p>7 rows × 199 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 mean           sd  cv_1_valid  cv_2_valid  \\\n",
       "0                     mae   17.570190    16.006790    9.329830    8.163634   \n",
       "1  mean_residual_deviance  563.621700  1195.858400   87.045720   66.644930   \n",
       "2                     mse  563.621700  1195.858400   87.045720   66.644930   \n",
       "3                      r2        -inf          NaN        -inf        -inf   \n",
       "4       residual_deviance  563.621700  1195.858400   87.045720   66.644930   \n",
       "5                    rmse   17.570190    16.006790    9.329830    8.163634   \n",
       "6                   rmsle    0.039509     0.039005    0.019392    0.017479   \n",
       "\n",
       "   cv_3_valid  cv_4_valid  cv_5_valid  cv_6_valid  cv_7_valid  ...  \\\n",
       "0    6.054688   17.979470    5.881683   14.246048   10.718098  ...   \n",
       "1   36.659240  323.261320   34.594196  202.949890  114.877625  ...   \n",
       "2   36.659240  323.261320   34.594196  202.949890  114.877625  ...   \n",
       "3        -inf        -inf        -inf        -inf        -inf  ...   \n",
       "4   36.659240  323.261320   34.594196  202.949890  114.877625  ...   \n",
       "5    6.054688   17.979470    5.881683   14.246048   10.718098  ...   \n",
       "6    0.012361    0.039438    0.012292    0.029666    0.022220  ...   \n",
       "\n",
       "   cv_187_valid  cv_188_valid  cv_189_valid  cv_190_valid  cv_191_valid  \\\n",
       "0     39.714485     12.618328      0.134911     22.057250     39.063625   \n",
       "1   1577.240200    159.222200      0.018201    486.522200   1525.966800   \n",
       "2   1577.240200    159.222200      0.018201    486.522200   1525.966800   \n",
       "3          -inf          -inf          -inf          -inf          -inf   \n",
       "4   1577.240200    159.222200      0.018201    486.522200   1525.966800   \n",
       "5     39.714485     12.618328      0.134911     22.057250     39.063625   \n",
       "6      0.086741      0.026442      0.000282      0.047909      0.082595   \n",
       "\n",
       "   cv_192_valid  cv_193_valid  cv_194_valid  cv_195_valid  cv_196_valid  \n",
       "0      3.960656     17.750435     16.650349     32.196976      8.770913  \n",
       "1     15.686793    315.077940    277.234100   1036.645100     76.928925  \n",
       "2     15.686793    315.077940    277.234100   1036.645100     76.928925  \n",
       "3          -inf          -inf          -inf          -inf          -inf  \n",
       "4     15.686793    315.077940    277.234100   1036.645100     76.928925  \n",
       "5      3.960656     17.750435     16.650349     32.196976      8.770913  \n",
       "6      0.008868      0.040064      0.034965      0.073204      0.019685  \n",
       "\n",
       "[7 rows x 199 columns]"
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     "text": [
      "\n",
      "Scoring History: \n"
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       "      <td>14.014 sec</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.432130</td>\n",
       "      <td>17.473638</td>\n",
       "      <td>549.064730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>2023-03-14 11:21:21</td>\n",
       "      <td>14.019 sec</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.394158</td>\n",
       "      <td>16.595468</td>\n",
       "      <td>501.498304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>2023-03-14 11:21:21</td>\n",
       "      <td>14.023 sec</td>\n",
       "      <td>30.0</td>\n",
       "      <td>21.661557</td>\n",
       "      <td>16.073638</td>\n",
       "      <td>469.223070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>2023-03-14 11:21:21</td>\n",
       "      <td>14.028 sec</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.151922</td>\n",
       "      <td>15.626474</td>\n",
       "      <td>447.403810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>2023-03-14 11:21:21</td>\n",
       "      <td>14.033 sec</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.634538</td>\n",
       "      <td>15.240241</td>\n",
       "      <td>425.784150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>2023-03-14 11:21:21</td>\n",
       "      <td>14.038 sec</td>\n",
       "      <td>45.0</td>\n",
       "      <td>20.071959</td>\n",
       "      <td>14.767749</td>\n",
       "      <td>402.883552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>2023-03-14 11:21:21</td>\n",
       "      <td>14.041 sec</td>\n",
       "      <td>47.0</td>\n",
       "      <td>19.819350</td>\n",
       "      <td>14.602886</td>\n",
       "      <td>392.806625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp    duration  number_of_trees  training_rmse  \\\n",
       "0     2023-03-14 11:21:21  13.994 sec              0.0      30.117082   \n",
       "1     2023-03-14 11:21:21  13.999 sec              5.0      27.373106   \n",
       "2     2023-03-14 11:21:21  14.004 sec             10.0      25.841602   \n",
       "3     2023-03-14 11:21:21  14.009 sec             15.0      24.585308   \n",
       "4     2023-03-14 11:21:21  14.014 sec             20.0      23.432130   \n",
       "5     2023-03-14 11:21:21  14.019 sec             25.0      22.394158   \n",
       "6     2023-03-14 11:21:21  14.023 sec             30.0      21.661557   \n",
       "7     2023-03-14 11:21:21  14.028 sec             35.0      21.151922   \n",
       "8     2023-03-14 11:21:21  14.033 sec             40.0      20.634538   \n",
       "9     2023-03-14 11:21:21  14.038 sec             45.0      20.071959   \n",
       "10    2023-03-14 11:21:21  14.041 sec             47.0      19.819350   \n",
       "\n",
       "    training_mae  training_deviance  \n",
       "0      22.904332         907.038609  \n",
       "1      20.515805         749.286912  \n",
       "2      19.479880         667.788369  \n",
       "3      18.524008         604.437355  \n",
       "4      17.473638         549.064730  \n",
       "5      16.595468         501.498304  \n",
       "6      16.073638         469.223070  \n",
       "7      15.626474         447.403810  \n",
       "8      15.240241         425.784150  \n",
       "9      14.767749         402.883552  \n",
       "10     14.602886         392.806625  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Variable Importances: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>relative_importance</th>\n",
       "      <th>scaled_importance</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>skill_mean</td>\n",
       "      <td>134863.937500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.256808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p1_duration_mean</td>\n",
       "      <td>84773.695312</td>\n",
       "      <td>0.628587</td>\n",
       "      <td>0.161426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>social_mean</td>\n",
       "      <td>50070.097656</td>\n",
       "      <td>0.371264</td>\n",
       "      <td>0.095344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>skill_std</td>\n",
       "      <td>46421.835938</td>\n",
       "      <td>0.344212</td>\n",
       "      <td>0.088397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p1_efficiency_mean</td>\n",
       "      <td>42921.980469</td>\n",
       "      <td>0.318261</td>\n",
       "      <td>0.081732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mean_zscore_int_solns_per_min</td>\n",
       "      <td>33753.937500</td>\n",
       "      <td>0.250281</td>\n",
       "      <td>0.064274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mean_zscore_num_postcomplete</td>\n",
       "      <td>30145.832031</td>\n",
       "      <td>0.223528</td>\n",
       "      <td>0.057404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>age_mean</td>\n",
       "      <td>27965.187500</td>\n",
       "      <td>0.207359</td>\n",
       "      <td>0.053251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cogstyle_diversity</td>\n",
       "      <td>19712.869141</td>\n",
       "      <td>0.146169</td>\n",
       "      <td>0.037537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>p1_efficiency_std</td>\n",
       "      <td>13534.050781</td>\n",
       "      <td>0.100353</td>\n",
       "      <td>0.025772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>p1_duration_std</td>\n",
       "      <td>13244.295898</td>\n",
       "      <td>0.098205</td>\n",
       "      <td>0.025220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>age_std</td>\n",
       "      <td>9351.203125</td>\n",
       "      <td>0.069338</td>\n",
       "      <td>0.017807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>n_college</td>\n",
       "      <td>6670.204102</td>\n",
       "      <td>0.049459</td>\n",
       "      <td>0.012701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>social_std</td>\n",
       "      <td>5011.611328</td>\n",
       "      <td>0.037160</td>\n",
       "      <td>0.009543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mean_nominal_real_gap</td>\n",
       "      <td>3444.320068</td>\n",
       "      <td>0.025539</td>\n",
       "      <td>0.006559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>p1q2_tolconflict_div</td>\n",
       "      <td>2362.433838</td>\n",
       "      <td>0.017517</td>\n",
       "      <td>0.004499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>p1q3_optimization_div</td>\n",
       "      <td>769.092896</td>\n",
       "      <td>0.005703</td>\n",
       "      <td>0.001465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>p1q1_exploration_div</td>\n",
       "      <td>137.372040</td>\n",
       "      <td>0.001019</td>\n",
       "      <td>0.000262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>n_female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         variable  relative_importance  scaled_importance  \\\n",
       "0                      skill_mean        134863.937500           1.000000   \n",
       "1                p1_duration_mean         84773.695312           0.628587   \n",
       "2                     social_mean         50070.097656           0.371264   \n",
       "3                       skill_std         46421.835938           0.344212   \n",
       "4              p1_efficiency_mean         42921.980469           0.318261   \n",
       "5   mean_zscore_int_solns_per_min         33753.937500           0.250281   \n",
       "6    mean_zscore_num_postcomplete         30145.832031           0.223528   \n",
       "7                        age_mean         27965.187500           0.207359   \n",
       "8              cogstyle_diversity         19712.869141           0.146169   \n",
       "9               p1_efficiency_std         13534.050781           0.100353   \n",
       "10                p1_duration_std         13244.295898           0.098205   \n",
       "11                        age_std          9351.203125           0.069338   \n",
       "12                      n_college          6670.204102           0.049459   \n",
       "13                     social_std          5011.611328           0.037160   \n",
       "14          mean_nominal_real_gap          3444.320068           0.025539   \n",
       "15           p1q2_tolconflict_div          2362.433838           0.017517   \n",
       "16          p1q3_optimization_div           769.092896           0.005703   \n",
       "17           p1q1_exploration_div           137.372040           0.001019   \n",
       "18                       n_female             0.000000           0.000000   \n",
       "\n",
       "    percentage  \n",
       "0     0.256808  \n",
       "1     0.161426  \n",
       "2     0.095344  \n",
       "3     0.088397  \n",
       "4     0.081732  \n",
       "5     0.064274  \n",
       "6     0.057404  \n",
       "7     0.053251  \n",
       "8     0.037537  \n",
       "9     0.025772  \n",
       "10    0.025220  \n",
       "11    0.017807  \n",
       "12    0.012701  \n",
       "13    0.009543  \n",
       "14    0.006559  \n",
       "15    0.004499  \n",
       "16    0.001465  \n",
       "17    0.000262  \n",
       "18    0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o.init()\n",
    "\n",
    "df_oos = pd.read_pickle(\"prediction_features.pkl\")\n",
    "full_features = ['n_female', 'n_college', 'p1q1_exploration_div', 'p1q2_tolconflict_div', 'p1q3_optimization_div', \n",
    "                 'age_mean', 'age_std', 'skill_mean', 'skill_std', 'social_mean', 'social_std', \n",
    "                 'mean_nominal_real_gap','mean_zscore_int_solns_per_min', 'mean_zscore_num_postcomplete', 'cogstyle_diversity', \n",
    "                 'p1_efficiency_mean', 'p1_efficiency_std', 'p1_duration_mean', 'p1_duration_std']\n",
    "\n",
    "hf = h2o.H2OFrame(df_oos)\n",
    "\n",
    "# Run AutoML with LOO CV and keep_cross_validation_predictions, to be used for Q2 calculations \n",
    "aml = H2OAutoML(max_models=100, seed=2022, nfolds=len(df_oos), \n",
    "                keep_cross_validation_predictions=True, keep_cross_validation_fold_assignment=True,\n",
    "                exclude_algos = [\"DeepLearning\", \"XGBoost\"], verbosity=\"info\")\n",
    "\n",
    "aml.train(x=full_features, \n",
    "          y=\"p2_cumulative_normscore\", \n",
    "          training_frame=hf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = aml.leaderboard.as_data_frame()['model_id']\n",
    "\n",
    "q2_means = []\n",
    "\n",
    "for loo_index in df_oos.index:\n",
    "    q2_means.append(df_oos.drop(loo_index)[\"p2_cumulative_normscore\"].mean())\n",
    "    \n",
    "    \n",
    "q2_values = []\n",
    "\n",
    "for model_name in model_names: \n",
    "    model = h2o.get_model(model_name)\n",
    "    q2 = 1 - np.sum((h2o.as_list(model.cross_validation_holdout_predictions())[\"predict\"] - df_oos[\"p2_cumulative_normscore\"])**2) / np.sum((np.array(q2_means) - df_oos[\"p2_cumulative_normscore\"])**2)\n",
    "    q2_values.append(q2)\n",
    "    \n",
    "    \n",
    "h2o.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure(figsize=(8,8)).gca()\n",
    "ax.yaxis.get_major_locator().set_params(integer=True)\n",
    "\n",
    "plt.hist(q2_values, bins=10, alpha=0.4, edgecolor=None)\n",
    "plt.axvline(x=np.mean(q2_values), ymax=15, linestyle=\"--\", color=\"black\")\n",
    "plt.ylim(-.01,25)\n",
    "plt.tick_params(labelsize=25)\n",
    "plt.xlabel(\"$Q^2$\", fontsize=25)\n",
    "plt.ylabel(\"Count\", fontsize=25)\n",
    "\n",
    "plt.margins(x=0.1, y=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4537815798758368, 0.36113838478878335)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(q2_values), np.mean(q2_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true"
   },
   "source": [
    "# Figure 4: Out-of-sample permutation feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_featureimportance = pd.read_pickle(\"prediction_features.pkl\")\n",
    "\n",
    "full_features = ['n_female', 'n_college', 'p1q1_exploration_div', 'p1q2_tolconflict_div',\n",
    "                 'p1q3_optimization_div', 'age_mean', 'age_std', 'skill_mean', 'skill_std',\n",
    "                 'social_mean', 'social_std', 'mean_nominal_real_gap',\n",
    "                 'mean_zscore_int_solns_per_min', 'mean_zscore_num_postcomplete',\n",
    "                 'cogstyle_diversity', 'p1_efficiency_mean', 'p1_efficiency_std',\n",
    "                 'p1_duration_mean', 'p1_duration_std']\n",
    "\n",
    "truncated_features = [\"skill_mean\", \"p1q3_optimization_div\", \"skill_std\", \"social_mean\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def featimp_baseline_models(df, estimator, feature_cols, outcome_col):\n",
    "    baseline_models = []\n",
    "    \n",
    "    for loo_index in tqdm(df.index):\n",
    "        if hasattr(estimator, \"random_state\"):\n",
    "            estimator.random_state = loo_index\n",
    "            \n",
    "        baseline_models.append(deepcopy(estimator.fit(X=df.drop(loo_index)[feature_cols].values, y=df.drop(loo_index)[outcome_col])))\n",
    "        \n",
    "    return baseline_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def q2_score_models(df, model_list, feature_cols, outcome_col):\n",
    "    q2_means = []\n",
    "    q2_preds = []\n",
    "    \n",
    "    for loo_index in df.index:\n",
    "        q2_means.append(df.drop(loo_index)[outcome_col].mean())\n",
    "        q2_preds.append(model_list[loo_index].predict(np.array(df.iloc[loo_index][feature_cols]).reshape(1,-1))[0])\n",
    "    \n",
    "    q2 = 1 - np.sum((np.array(q2_preds) - df[outcome_col])**2) / np.sum((np.array(q2_means) - df[outcome_col])**2)\n",
    "    \n",
    "    return q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def q2_feature_importance(df, estimator, feature_cols, outcome_col, n_permutations):\n",
    "    \n",
    "    baseline_models = featimp_baseline_models(df, estimator, feature_cols, outcome_col)\n",
    "    baseline_q2 = q2_score_models(df, baseline_models, feature_cols, outcome_col)\n",
    "    \n",
    "    feature_q2_values = {}\n",
    "    \n",
    "    for feature in tqdm(feature_cols, desc=\" outer\", position=0, leave=True):\n",
    "        df_permutation = df.copy(deep=True)\n",
    "        feature_q2 = []\n",
    "        \n",
    "        for permutation in tqdm(range(n_permutations), desc=\" inner loop\", position=1, leave=False):\n",
    "            df_permutation[feature] = df_permutation[feature].sample(frac=1, ignore_index=True, random_state=permutation)\n",
    "            feature_q2.append(q2_score_models(df_permutation, baseline_models, feature_cols, outcome_col))\n",
    "        \n",
    "        feature_q2_values[feature] = feature_q2\n",
    "        \n",
    "    return baseline_q2, pd.DataFrame(feature_q2_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 196/196 [00:00<00:00, 531.76it/s]\n",
      " outer:   0%|                                                                                                                                                                                                                                             | 0/4 [00:00<?, ?it/s]\n",
      " inner loop:   0%|                                                                                                                                                                                                                                       | 0/30 [00:00<?, ?it/s]\u001b[A\n",
      " inner loop:   3%|███████▍                                                                                                                                                                                                                       | 1/30 [00:00<00:05,  5.61it/s]\u001b[A\n",
      " inner loop:   7%|██████████████▊                                                                                                                                                                                                                | 2/30 [00:00<00:04,  5.65it/s]\u001b[A\n",
      " inner loop:  10%|██████████████████████▎                                                                                                                                                                                                        | 3/30 [00:00<00:04,  5.80it/s]\u001b[A\n",
      " inner loop:  13%|█████████████████████████████▋                                                                                                                                                                                                 | 4/30 [00:00<00:04,  5.86it/s]\u001b[A\n",
      " inner loop:  17%|█████████████████████████████████████▏                                                                                                                                                                                         | 5/30 [00:00<00:04,  5.95it/s]\u001b[A\n",
      " inner loop:  20%|████████████████████████████████████████████▌                                                                                                                                                                                  | 6/30 [00:01<00:03,  6.03it/s]\u001b[A\n",
      " inner loop:  23%|████████████████████████████████████████████████████                                                                                                                                                                           | 7/30 [00:01<00:03,  6.10it/s]\u001b[A\n",
      " inner loop:  27%|███████████████████████████████████████████████████████████▍                                                                                                                                                                   | 8/30 [00:01<00:03,  6.01it/s]\u001b[A\n",
      " inner loop:  30%|██████████████████████████████████████████████████████████████████▉                                                                                                                                                            | 9/30 [00:01<00:03,  6.07it/s]\u001b[A\n",
      " inner loop:  33%|██████████████████████████████████████████████████████████████████████████                                                                                                                                                    | 10/30 [00:01<00:03,  6.00it/s]\u001b[A\n",
      " inner loop:  37%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                            | 11/30 [00:01<00:03,  6.05it/s]\u001b[A\n",
      " inner loop:  40%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                     | 12/30 [00:02<00:03,  5.99it/s]\u001b[A\n",
      " inner loop:  43%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                             | 13/30 [00:02<00:02,  5.88it/s]\u001b[A\n",
      " inner loop:  47%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                      | 14/30 [00:02<00:02,  5.91it/s]\u001b[A\n",
      " inner loop:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                               | 15/30 [00:02<00:02,  5.95it/s]\u001b[A\n",
      " inner loop:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 16/30 [00:02<00:02,  5.97it/s]\u001b[A\n",
      " inner loop:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 17/30 [00:02<00:02,  6.02it/s]\u001b[A\n",
      " inner loop:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                        | 18/30 [00:03<00:01,  6.07it/s]\u001b[A\n",
      " inner loop:  63%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                 | 19/30 [00:03<00:01,  6.02it/s]\u001b[A\n",
      " inner loop:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                          | 20/30 [00:03<00:01,  5.76it/s]\u001b[A\n",
      " inner loop:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                  | 21/30 [00:03<00:01,  5.85it/s]\u001b[A\n",
      " inner loop:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                           | 22/30 [00:03<00:01,  5.95it/s]\u001b[A\n",
      " inner loop:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                   | 23/30 [00:03<00:01,  5.98it/s]\u001b[A\n",
      " inner loop:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                            | 24/30 [00:04<00:00,  6.01it/s]\u001b[A\n",
      " inner loop:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 25/30 [00:04<00:00,  6.03it/s]\u001b[A\n",
      " inner loop:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                             | 26/30 [00:04<00:00,  6.02it/s]\u001b[A\n",
      " inner loop:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                      | 27/30 [00:04<00:00,  6.02it/s]\u001b[A\n",
      " inner loop:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏              | 28/30 [00:04<00:00,  6.03it/s]\u001b[A\n",
      " inner loop:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌       | 29/30 [00:04<00:00,  6.02it/s]\u001b[A\n",
      " inner loop: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [00:05<00:00,  5.96it/s]\u001b[A\n",
      " outer:  25%|█████████████████████████████████████████████████████████▎                                                                                                                                                                           | 1/4 [00:05<00:15,  5.04s/it]\u001b[A\n",
      " inner loop:   0%|                                                                                                                                                                                                                                       | 0/30 [00:00<?, ?it/s]\u001b[A\n",
      " inner loop:   3%|███████▍                                                                                                                                                                                                                       | 1/30 [00:00<00:05,  5.56it/s]\u001b[A\n",
      " inner loop:   7%|██████████████▊                                                                                                                                                                                                                | 2/30 [00:00<00:04,  5.82it/s]\u001b[A\n",
      " inner loop:  10%|██████████████████████▎                                                                                                                                                                                                        | 3/30 [00:00<00:04,  6.02it/s]\u001b[A\n",
      " inner loop:  13%|█████████████████████████████▋                                                                                                                                                                                                 | 4/30 [00:00<00:04,  6.00it/s]\u001b[A\n",
      " inner loop:  17%|█████████████████████████████████████▏                                                                                                                                                                                         | 5/30 [00:00<00:04,  6.10it/s]\u001b[A\n",
      " inner loop:  20%|████████████████████████████████████████████▌                                                                                                                                                                                  | 6/30 [00:00<00:03,  6.14it/s]\u001b[A\n",
      " inner loop:  23%|████████████████████████████████████████████████████                                                                                                                                                                           | 7/30 [00:01<00:03,  6.11it/s]\u001b[A\n",
      " inner loop:  27%|███████████████████████████████████████████████████████████▍                                                                                                                                                                   | 8/30 [00:01<00:03,  6.08it/s]\u001b[A\n",
      " inner loop:  30%|██████████████████████████████████████████████████████████████████▉                                                                                                                                                            | 9/30 [00:01<00:03,  6.11it/s]\u001b[A\n",
      " inner loop:  33%|██████████████████████████████████████████████████████████████████████████                                                                                                                                                    | 10/30 [00:01<00:03,  5.96it/s]\u001b[A\n",
      " inner loop:  37%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                            | 11/30 [00:01<00:03,  5.90it/s]\u001b[A\n",
      " inner loop:  40%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                     | 12/30 [00:01<00:03,  5.98it/s]\u001b[A\n",
      " inner loop:  43%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                             | 13/30 [00:02<00:02,  6.00it/s]\u001b[A\n",
      " inner loop:  47%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                      | 14/30 [00:02<00:02,  6.03it/s]\u001b[A\n",
      " inner loop:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                               | 15/30 [00:02<00:02,  6.02it/s]\u001b[A\n",
      " inner loop:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 16/30 [00:02<00:02,  5.91it/s]\u001b[A\n",
      " inner loop:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 17/30 [00:02<00:02,  5.81it/s]\u001b[A\n",
      " inner loop:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                        | 18/30 [00:03<00:02,  5.65it/s]\u001b[A\n",
      " inner loop:  63%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                 | 19/30 [00:03<00:01,  5.59it/s]\u001b[A\n",
      " inner loop:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                          | 20/30 [00:03<00:01,  5.58it/s]\u001b[A\n",
      " inner loop:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                  | 21/30 [00:03<00:01,  5.65it/s]\u001b[A\n",
      " inner loop:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                           | 22/30 [00:03<00:01,  5.74it/s]\u001b[A\n",
      " inner loop:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                   | 23/30 [00:03<00:01,  5.82it/s]\u001b[A\n",
      " inner loop:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                            | 24/30 [00:04<00:01,  5.88it/s]\u001b[A\n",
      " inner loop:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 25/30 [00:04<00:00,  5.98it/s]\u001b[A\n",
      " inner loop:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                             | 26/30 [00:04<00:00,  5.94it/s]\u001b[A\n",
      " inner loop:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                      | 27/30 [00:04<00:00,  5.88it/s]\u001b[A\n",
      " inner loop:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏              | 28/30 [00:04<00:00,  5.73it/s]\u001b[A\n",
      " inner loop:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌       | 29/30 [00:04<00:00,  5.47it/s]\u001b[A\n",
      " inner loop: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [00:05<00:00,  5.39it/s]\u001b[A\n",
      " outer:  50%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                  | 2/4 [00:10<00:10,  5.11s/it]\u001b[A\n",
      " inner loop:   0%|                                                                                                                                                                                                                                       | 0/30 [00:00<?, ?it/s]\u001b[A\n",
      " inner loop:   3%|███████▍                                                                                                                                                                                                                       | 1/30 [00:00<00:05,  5.18it/s]\u001b[A\n",
      " inner loop:   7%|██████████████▊                                                                                                                                                                                                                | 2/30 [00:00<00:05,  5.09it/s]\u001b[A\n",
      " inner loop:  10%|██████████████████████▎                                                                                                                                                                                                        | 3/30 [00:00<00:05,  5.18it/s]\u001b[A\n",
      " inner loop:  13%|█████████████████████████████▋                                                                                                                                                                                                 | 4/30 [00:00<00:04,  5.34it/s]\u001b[A\n",
      " inner loop:  17%|█████████████████████████████████████▏                                                                                                                                                                                         | 5/30 [00:00<00:04,  5.39it/s]\u001b[A\n",
      " inner loop:  20%|████████████████████████████████████████████▌                                                                                                                                                                                  | 6/30 [00:01<00:04,  4.98it/s]\u001b[A\n",
      " inner loop:  23%|████████████████████████████████████████████████████                                                                                                                                                                           | 7/30 [00:01<00:04,  4.81it/s]\u001b[A\n",
      " inner loop:  27%|███████████████████████████████████████████████████████████▍                                                                                                                                                                   | 8/30 [00:01<00:04,  4.70it/s]\u001b[A\n",
      " inner loop:  30%|██████████████████████████████████████████████████████████████████▉                                                                                                                                                            | 9/30 [00:01<00:04,  4.81it/s]\u001b[A\n",
      " inner loop:  33%|██████████████████████████████████████████████████████████████████████████                                                                                                                                                    | 10/30 [00:02<00:04,  4.91it/s]\u001b[A\n",
      " inner loop:  37%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                            | 11/30 [00:02<00:03,  5.02it/s]\u001b[A\n",
      " inner loop:  40%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                     | 12/30 [00:02<00:03,  5.17it/s]\u001b[A\n",
      " inner loop:  43%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                             | 13/30 [00:02<00:03,  5.27it/s]\u001b[A\n",
      " inner loop:  47%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                      | 14/30 [00:02<00:03,  5.24it/s]\u001b[A\n",
      " inner loop:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                               | 15/30 [00:02<00:02,  5.30it/s]\u001b[A\n",
      " inner loop:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 16/30 [00:03<00:02,  5.48it/s]\u001b[A\n",
      " inner loop:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 17/30 [00:03<00:02,  5.50it/s]\u001b[A\n",
      " inner loop:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                        | 18/30 [00:03<00:02,  5.60it/s]\u001b[A\n",
      " inner loop:  63%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                 | 19/30 [00:03<00:01,  5.55it/s]\u001b[A\n",
      " inner loop:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                          | 20/30 [00:03<00:01,  5.72it/s]\u001b[A\n",
      " inner loop:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                  | 21/30 [00:03<00:01,  5.90it/s]\u001b[A\n",
      " inner loop:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                           | 22/30 [00:04<00:01,  6.04it/s]\u001b[A\n",
      " inner loop:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                   | 23/30 [00:04<00:01,  6.19it/s]\u001b[A\n",
      " inner loop:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                            | 24/30 [00:04<00:00,  6.25it/s]\u001b[A\n",
      " inner loop:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 25/30 [00:04<00:00,  6.09it/s]\u001b[A\n",
      " inner loop:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                             | 26/30 [00:04<00:00,  6.19it/s]\u001b[A\n",
      " inner loop:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                      | 27/30 [00:04<00:00,  6.28it/s]\u001b[A\n",
      " inner loop:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏              | 28/30 [00:05<00:00,  6.31it/s]\u001b[A\n",
      " inner loop:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌       | 29/30 [00:05<00:00,  6.30it/s]\u001b[A\n",
      " inner loop: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [00:05<00:00,  6.34it/s]\u001b[A\n",
      " outer:  75%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                         | 3/4 [00:15<00:05,  5.24s/it]\u001b[A\n",
      " inner loop:   0%|                                                                                                                                                                                                                                       | 0/30 [00:00<?, ?it/s]\u001b[A\n",
      " inner loop:   3%|███████▍                                                                                                                                                                                                                       | 1/30 [00:00<00:04,  6.51it/s]\u001b[A\n",
      " inner loop:   7%|██████████████▊                                                                                                                                                                                                                | 2/30 [00:00<00:04,  6.42it/s]\u001b[A\n",
      " inner loop:  10%|██████████████████████▎                                                                                                                                                                                                        | 3/30 [00:00<00:04,  6.42it/s]\u001b[A\n",
      " inner loop:  13%|█████████████████████████████▋                                                                                                                                                                                                 | 4/30 [00:00<00:04,  6.38it/s]\u001b[A\n",
      " inner loop:  17%|█████████████████████████████████████▏                                                                                                                                                                                         | 5/30 [00:00<00:03,  6.37it/s]\u001b[A\n",
      " inner loop:  20%|████████████████████████████████████████████▌                                                                                                                                                                                  | 6/30 [00:00<00:03,  6.06it/s]\u001b[A\n",
      " inner loop:  23%|████████████████████████████████████████████████████                                                                                                                                                                           | 7/30 [00:01<00:03,  6.18it/s]\u001b[A\n",
      " inner loop:  27%|███████████████████████████████████████████████████████████▍                                                                                                                                                                   | 8/30 [00:01<00:03,  6.24it/s]\u001b[A\n",
      " inner loop:  30%|██████████████████████████████████████████████████████████████████▉                                                                                                                                                            | 9/30 [00:01<00:03,  6.28it/s]\u001b[A\n",
      " inner loop:  33%|██████████████████████████████████████████████████████████████████████████                                                                                                                                                    | 10/30 [00:01<00:03,  6.37it/s]\u001b[A\n",
      " inner loop:  37%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                            | 11/30 [00:01<00:03,  6.22it/s]\u001b[A\n",
      " inner loop:  40%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                     | 12/30 [00:01<00:02,  6.20it/s]\u001b[A\n",
      " inner loop:  43%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                             | 13/30 [00:02<00:02,  6.25it/s]\u001b[A\n",
      " inner loop:  47%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                      | 14/30 [00:02<00:02,  6.25it/s]\u001b[A\n",
      " inner loop:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                               | 15/30 [00:02<00:02,  5.98it/s]\u001b[A\n",
      " inner loop:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 16/30 [00:02<00:02,  6.02it/s]\u001b[A\n",
      " inner loop:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 17/30 [00:02<00:02,  5.89it/s]\u001b[A\n",
      " inner loop:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                        | 18/30 [00:02<00:02,  5.99it/s]\u001b[A\n",
      " inner loop:  63%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                 | 19/30 [00:03<00:01,  5.86it/s]\u001b[A\n",
      " inner loop:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                          | 20/30 [00:03<00:01,  6.00it/s]\u001b[A\n",
      " inner loop:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                  | 21/30 [00:03<00:01,  6.02it/s]\u001b[A\n",
      " inner loop:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                           | 22/30 [00:03<00:01,  5.92it/s]\u001b[A\n",
      " inner loop:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                   | 23/30 [00:03<00:01,  6.07it/s]\u001b[A\n",
      " inner loop:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                            | 24/30 [00:03<00:00,  6.09it/s]\u001b[A\n",
      " inner loop:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 25/30 [00:04<00:00,  6.13it/s]\u001b[A\n",
      " inner loop:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                             | 26/30 [00:04<00:00,  6.22it/s]\u001b[A\n",
      " inner loop:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                      | 27/30 [00:04<00:00,  6.27it/s]\u001b[A\n",
      " inner loop:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏              | 28/30 [00:04<00:00,  6.24it/s]\u001b[A\n",
      " inner loop:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌       | 29/30 [00:04<00:00,  6.20it/s]\u001b[A\n",
      " inner loop: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [00:04<00:00,  6.24it/s]\u001b[A\n",
      " outer: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:20<00:00,  5.12s/it]\u001b[A\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 196/196 [00:20<00:00,  9.51it/s]\n",
      " outer:   0%|                                                                                                                                                                                                                                             | 0/4 [00:00<?, ?it/s]\n",
      " inner loop:   0%|                                                                                                                                                                                                                                       | 0/30 [00:00<?, ?it/s]\u001b[A\n",
      " inner loop:   3%|███████▍                                                                                                                                                                                                                       | 1/30 [00:01<00:41,  1.44s/it]\u001b[A\n",
      " inner loop:   7%|██████████████▊                                                                                                                                                                                                                | 2/30 [00:02<00:38,  1.38s/it]\u001b[A\n",
      " inner loop:  10%|██████████████████████▎                                                                                                                                                                                                        | 3/30 [00:04<00:35,  1.32s/it]\u001b[A\n",
      " inner loop:  13%|█████████████████████████████▋                                                                                                                                                                                                 | 4/30 [00:05<00:33,  1.28s/it]\u001b[A\n",
      " inner loop:  17%|█████████████████████████████████████▏                                                                                                                                                                                         | 5/30 [00:06<00:31,  1.28s/it]\u001b[A\n",
      " inner loop:  20%|████████████████████████████████████████████▌                                                                                                                                                                                  | 6/30 [00:07<00:30,  1.27s/it]\u001b[A\n",
      " inner loop:  23%|████████████████████████████████████████████████████                                                                                                                                                                           | 7/30 [00:09<00:28,  1.26s/it]\u001b[A\n",
      " inner loop:  27%|███████████████████████████████████████████████████████████▍                                                                                                                                                                   | 8/30 [00:10<00:27,  1.25s/it]\u001b[A\n",
      " inner loop:  30%|██████████████████████████████████████████████████████████████████▉                                                                                                                                                            | 9/30 [00:11<00:26,  1.25s/it]\u001b[A\n",
      " inner loop:  33%|██████████████████████████████████████████████████████████████████████████                                                                                                                                                    | 10/30 [00:12<00:24,  1.25s/it]\u001b[A\n",
      " inner loop:  37%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                            | 11/30 [00:13<00:23,  1.25s/it]\u001b[A\n",
      " inner loop:  40%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                     | 12/30 [00:15<00:22,  1.25s/it]\u001b[A\n",
      " inner loop:  43%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                             | 13/30 [00:16<00:21,  1.26s/it]\u001b[A\n",
      " inner loop:  47%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                      | 14/30 [00:17<00:20,  1.26s/it]\u001b[A\n",
      " inner loop:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                               | 15/30 [00:19<00:18,  1.26s/it]\u001b[A\n",
      " inner loop:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 16/30 [00:20<00:17,  1.25s/it]\u001b[A\n",
      " inner loop:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 17/30 [00:21<00:16,  1.25s/it]\u001b[A\n",
      " inner loop:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                        | 18/30 [00:22<00:14,  1.25s/it]\u001b[A\n",
      " inner loop:  63%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                 | 19/30 [00:23<00:13,  1.24s/it]\u001b[A\n",
      " inner loop:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                          | 20/30 [00:25<00:12,  1.24s/it]\u001b[A\n",
      " inner loop:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                  | 21/30 [00:26<00:11,  1.24s/it]\u001b[A\n",
      " inner loop:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                           | 22/30 [00:27<00:10,  1.25s/it]\u001b[A\n",
      " inner loop:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                   | 23/30 [00:29<00:08,  1.26s/it]\u001b[A\n",
      " inner loop:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                            | 24/30 [00:30<00:07,  1.26s/it]\u001b[A\n",
      " inner loop:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 25/30 [00:31<00:06,  1.25s/it]\u001b[A\n",
      " inner loop:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                             | 26/30 [00:32<00:04,  1.25s/it]\u001b[A\n",
      " inner loop:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                      | 27/30 [00:33<00:03,  1.24s/it]\u001b[A\n",
      " inner loop:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏              | 28/30 [00:35<00:02,  1.24s/it]\u001b[A\n",
      " inner loop:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌       | 29/30 [00:36<00:01,  1.24s/it]\u001b[A\n",
      " inner loop: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [00:37<00:00,  1.24s/it]\u001b[A\n",
      " outer:  25%|█████████████████████████████████████████████████████████▎                                                                                                                                                                           | 1/4 [00:37<01:53, 37.73s/it]\u001b[A\n",
      " inner loop:   0%|                                                                                                                                                                                                                                       | 0/30 [00:00<?, ?it/s]\u001b[A\n",
      " inner loop:   3%|███████▍                                                                                                                                                                                                                       | 1/30 [00:01<00:36,  1.25s/it]\u001b[A\n",
      " inner loop:   7%|██████████████▊                                                                                                                                                                                                                | 2/30 [00:02<00:34,  1.24s/it]\u001b[A\n",
      " inner loop:  10%|██████████████████████▎                                                                                                                                                                                                        | 3/30 [00:03<00:33,  1.25s/it]\u001b[A\n",
      " inner loop:  13%|█████████████████████████████▋                                                                                                                                                                                                 | 4/30 [00:04<00:32,  1.24s/it]\u001b[A\n",
      " inner loop:  17%|█████████████████████████████████████▏                                                                                                                                                                                         | 5/30 [00:06<00:30,  1.24s/it]\u001b[A\n",
      " inner loop:  20%|████████████████████████████████████████████▌                                                                                                                                                                                  | 6/30 [00:07<00:29,  1.24s/it]\u001b[A\n",
      " inner loop:  23%|████████████████████████████████████████████████████                                                                                                                                                                           | 7/30 [00:08<00:28,  1.25s/it]\u001b[A\n",
      " inner loop:  27%|███████████████████████████████████████████████████████████▍                                                                                                                                                                   | 8/30 [00:09<00:27,  1.25s/it]\u001b[A\n",
      " inner loop:  30%|██████████████████████████████████████████████████████████████████▉                                                                                                                                                            | 9/30 [00:11<00:26,  1.26s/it]\u001b[A\n",
      " inner loop:  33%|██████████████████████████████████████████████████████████████████████████                                                                                                                                                    | 10/30 [00:12<00:25,  1.26s/it]\u001b[A\n",
      " inner loop:  37%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                            | 11/30 [00:13<00:23,  1.26s/it]\u001b[A\n",
      " inner loop:  40%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                     | 12/30 [00:15<00:22,  1.26s/it]\u001b[A\n",
      " inner loop:  43%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                             | 13/30 [00:16<00:21,  1.28s/it]\u001b[A\n",
      " inner loop:  47%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                      | 14/30 [00:17<00:20,  1.29s/it]\u001b[A\n",
      " inner loop:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                               | 15/30 [00:19<00:19,  1.33s/it]\u001b[A\n",
      " inner loop:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 16/30 [00:20<00:18,  1.32s/it]\u001b[A\n",
      " inner loop:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 17/30 [00:21<00:16,  1.30s/it]\u001b[A\n",
      " inner loop:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                        | 18/30 [00:22<00:15,  1.29s/it]\u001b[A\n",
      " inner loop:  63%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                 | 19/30 [00:24<00:14,  1.28s/it]\u001b[A\n",
      " inner loop:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                          | 20/30 [00:25<00:12,  1.27s/it]\u001b[A\n",
      " inner loop:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                  | 21/30 [00:26<00:11,  1.26s/it]\u001b[A\n",
      " inner loop:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                           | 22/30 [00:27<00:10,  1.25s/it]\u001b[A\n",
      " inner loop:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                   | 23/30 [00:29<00:08,  1.25s/it]\u001b[A\n",
      " inner loop:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                            | 24/30 [00:30<00:07,  1.25s/it]\u001b[A\n",
      " inner loop:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 25/30 [00:31<00:06,  1.25s/it]\u001b[A\n",
      " inner loop:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                             | 26/30 [00:32<00:04,  1.24s/it]\u001b[A\n",
      " inner loop:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                      | 27/30 [00:34<00:03,  1.24s/it]\u001b[A\n",
      " inner loop:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏              | 28/30 [00:35<00:02,  1.24s/it]\u001b[A\n",
      " inner loop:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌       | 29/30 [00:36<00:01,  1.24s/it]\u001b[A\n",
      " inner loop: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [00:37<00:00,  1.28s/it]\u001b[A\n",
      " outer:  50%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                  | 2/4 [01:15<01:15, 37.86s/it]\u001b[A\n",
      " inner loop:   0%|                                                                                                                                                                                                                                       | 0/30 [00:00<?, ?it/s]\u001b[A\n",
      " inner loop:   3%|███████▍                                                                                                                                                                                                                       | 1/30 [00:01<00:47,  1.64s/it]\u001b[A\n",
      " inner loop:   7%|██████████████▊                                                                                                                                                                                                                | 2/30 [00:02<00:39,  1.40s/it]\u001b[A\n",
      " inner loop:  10%|██████████████████████▎                                                                                                                                                                                                        | 3/30 [00:04<00:35,  1.32s/it]\u001b[A\n",
      " inner loop:  13%|█████████████████████████████▋                                                                                                                                                                                                 | 4/30 [00:05<00:33,  1.28s/it]\u001b[A\n",
      " inner loop:  17%|█████████████████████████████████████▏                                                                                                                                                                                         | 5/30 [00:06<00:31,  1.26s/it]\u001b[A\n",
      " inner loop:  20%|████████████████████████████████████████████▌                                                                                                                                                                                  | 6/30 [00:07<00:29,  1.25s/it]\u001b[A\n",
      " inner loop:  23%|████████████████████████████████████████████████████                                                                                                                                                                           | 7/30 [00:09<00:28,  1.26s/it]\u001b[A\n",
      " inner loop:  27%|███████████████████████████████████████████████████████████▍                                                                                                                                                                   | 8/30 [00:10<00:28,  1.27s/it]\u001b[A\n",
      " inner loop:  30%|██████████████████████████████████████████████████████████████████▉                                                                                                                                                            | 9/30 [00:11<00:26,  1.26s/it]\u001b[A\n",
      " inner loop:  33%|██████████████████████████████████████████████████████████████████████████                                                                                                                                                    | 10/30 [00:12<00:24,  1.25s/it]\u001b[A\n",
      " inner loop:  37%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                            | 11/30 [00:14<00:23,  1.24s/it]\u001b[A\n",
      " inner loop:  40%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                     | 12/30 [00:15<00:22,  1.23s/it]\u001b[A\n",
      " inner loop:  43%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                             | 13/30 [00:16<00:20,  1.23s/it]\u001b[A\n",
      " inner loop:  47%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                      | 14/30 [00:17<00:19,  1.22s/it]\u001b[A\n",
      " inner loop:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                               | 15/30 [00:18<00:18,  1.23s/it]\u001b[A\n",
      " inner loop:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 16/30 [00:20<00:17,  1.23s/it]\u001b[A\n",
      " inner loop:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 17/30 [00:21<00:15,  1.23s/it]\u001b[A\n",
      " inner loop:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                        | 18/30 [00:22<00:14,  1.24s/it]\u001b[A\n",
      " inner loop:  63%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                 | 19/30 [00:23<00:13,  1.23s/it]\u001b[A\n",
      " inner loop:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                          | 20/30 [00:25<00:12,  1.23s/it]\u001b[A\n",
      " inner loop:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                  | 21/30 [00:26<00:11,  1.23s/it]\u001b[A\n",
      " inner loop:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                           | 22/30 [00:27<00:09,  1.23s/it]\u001b[A\n",
      " inner loop:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                   | 23/30 [00:28<00:08,  1.22s/it]\u001b[A\n",
      " inner loop:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                            | 24/30 [00:29<00:07,  1.22s/it]\u001b[A\n",
      " inner loop:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 25/30 [00:31<00:06,  1.23s/it]\u001b[A\n",
      " inner loop:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                             | 26/30 [00:32<00:04,  1.24s/it]\u001b[A\n",
      " inner loop:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                      | 27/30 [00:33<00:03,  1.23s/it]\u001b[A\n",
      " inner loop:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏              | 28/30 [00:34<00:02,  1.22s/it]\u001b[A\n",
      " inner loop:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌       | 29/30 [00:36<00:01,  1.24s/it]\u001b[A\n",
      " inner loop: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [00:37<00:00,  1.23s/it]\u001b[A\n",
      " outer:  75%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                         | 3/4 [01:53<00:37, 37.64s/it]\u001b[A\n",
      " inner loop:   0%|                                                                                                                                                                                                                                       | 0/30 [00:00<?, ?it/s]\u001b[A\n",
      " inner loop:   3%|███████▍                                                                                                                                                                                                                       | 1/30 [00:01<00:35,  1.22s/it]\u001b[A\n",
      " inner loop:   7%|██████████████▊                                                                                                                                                                                                                | 2/30 [00:02<00:34,  1.21s/it]\u001b[A\n",
      " inner loop:  10%|██████████████████████▎                                                                                                                                                                                                        | 3/30 [00:03<00:32,  1.22s/it]\u001b[A\n",
      " inner loop:  13%|█████████████████████████████▋                                                                                                                                                                                                 | 4/30 [00:04<00:32,  1.25s/it]\u001b[A\n",
      " inner loop:  17%|█████████████████████████████████████▏                                                                                                                                                                                         | 5/30 [00:06<00:32,  1.30s/it]\u001b[A\n",
      " inner loop:  20%|████████████████████████████████████████████▌                                                                                                                                                                                  | 6/30 [00:07<00:30,  1.28s/it]\u001b[A\n",
      " inner loop:  23%|████████████████████████████████████████████████████                                                                                                                                                                           | 7/30 [00:08<00:29,  1.29s/it]\u001b[A\n",
      " inner loop:  27%|███████████████████████████████████████████████████████████▍                                                                                                                                                                   | 8/30 [00:10<00:28,  1.30s/it]\u001b[A\n",
      " inner loop:  30%|██████████████████████████████████████████████████████████████████▉                                                                                                                                                            | 9/30 [00:11<00:26,  1.27s/it]\u001b[A\n",
      " inner loop:  33%|██████████████████████████████████████████████████████████████████████████                                                                                                                                                    | 10/30 [00:12<00:25,  1.25s/it]\u001b[A\n",
      " inner loop:  37%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                            | 11/30 [00:13<00:23,  1.24s/it]\u001b[A\n",
      " inner loop:  40%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                     | 12/30 [00:15<00:22,  1.24s/it]\u001b[A\n",
      " inner loop:  43%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                             | 13/30 [00:16<00:20,  1.23s/it]\u001b[A\n",
      " inner loop:  47%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                      | 14/30 [00:17<00:19,  1.24s/it]\u001b[A\n",
      " inner loop:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                               | 15/30 [00:18<00:18,  1.23s/it]\u001b[A\n",
      " inner loop:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 16/30 [00:20<00:17,  1.24s/it]\u001b[A\n",
      " inner loop:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 17/30 [00:21<00:16,  1.29s/it]\u001b[A\n",
      " inner loop:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                        | 18/30 [00:22<00:15,  1.33s/it]\u001b[A\n",
      " inner loop:  63%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                 | 19/30 [00:24<00:14,  1.32s/it]\u001b[A\n",
      " inner loop:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                          | 20/30 [00:25<00:12,  1.29s/it]\u001b[A\n",
      " inner loop:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                  | 21/30 [00:26<00:11,  1.26s/it]\u001b[A\n",
      " inner loop:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                           | 22/30 [00:27<00:09,  1.25s/it]\u001b[A\n",
      " inner loop:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                   | 23/30 [00:29<00:08,  1.25s/it]\u001b[A\n",
      " inner loop:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                            | 24/30 [00:30<00:07,  1.24s/it]\u001b[A\n",
      " inner loop:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 25/30 [00:31<00:06,  1.23s/it]\u001b[A\n",
      " inner loop:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                             | 26/30 [00:32<00:04,  1.23s/it]\u001b[A\n",
      " inner loop:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                      | 27/30 [00:33<00:03,  1.23s/it]\u001b[A\n",
      " inner loop:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏              | 28/30 [00:35<00:02,  1.23s/it]\u001b[A\n",
      " inner loop:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌       | 29/30 [00:36<00:01,  1.24s/it]\u001b[A\n",
      " inner loop: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 30/30 [00:37<00:00,  1.24s/it]\u001b[A\n",
      " outer: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [02:30<00:00, 37.68s/it]\u001b[A\n"
     ]
    }
   ],
   "source": [
    "baseline_LR, df_featimp_LR = q2_feature_importance(df_featureimportance, LinearRegression(), truncated_features, \"p2_cumulative_normscore\", 30)\n",
    "baseline_RF, df_featimp_RF = q2_feature_importance(df_featureimportance, RandomForestRegressor(), truncated_features, \"p2_cumulative_normscore\", 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_imp = 100 * (baseline_LR - df_featimp_LR)/baseline_LR\n",
    "feature_imp_vis_LR = pd.DataFrame([feature_imp.mean(axis=0),feature_imp.std(axis=0)]).T.rename(columns={0:\"mean_diff\", 1:\"diff_std\"}).sort_values(\"mean_diff\", ascending=True) \n",
    "\n",
    "feature_imp = 100 * (baseline_RF - df_featimp_RF)/baseline_RF\n",
    "feature_imp_vis_RF = pd.DataFrame([feature_imp.mean(axis=0),feature_imp.std(axis=0)]).T.rename(columns={0:\"mean_diff\", 1:\"diff_std\"}).sort_values(\"mean_diff\", ascending=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_imp_vis_LR.index = feature_imp_vis_LR.index.map(dict(zip(truncated_features, [\"Skill\", \"Cognitive style diversity\", \"Skill diversity\", \"Social perceptiveness\"])))\n",
    "feature_imp_vis_RF.index = feature_imp_vis_RF.index.map(dict(zip(truncated_features, [\"Skill\", \"Cognitive style diversity\", \"Skill diversity\", \"Social perceptiveness\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7fb4122700a0>,\n",
       "  <matplotlib.axis.XTick at 0x7fb412270280>,\n",
       "  <matplotlib.axis.XTick at 0x7fb40ddd8d90>,\n",
       "  <matplotlib.axis.XTick at 0x7fb41304e940>],\n",
       " [Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, '')])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "feature_imp_vis_RF = feature_imp_vis_RF.loc[feature_imp_vis_LR.index]\n",
    "\n",
    "plt.errorbar(x=feature_imp_vis_LR[\"mean_diff\"], \n",
    "             y=feature_imp_vis_LR.index, \n",
    "             xerr=1.96*feature_imp_vis_LR[\"diff_std\"], linestyle=\"\", label=\"Linear regression\", linewidth=3)\n",
    "plt.scatter(x=feature_imp_vis_LR['mean_diff'], y=feature_imp_vis_LR.index, s=150)\n",
    "\n",
    "plt.errorbar(x=feature_imp_vis_RF[\"mean_diff\"], \n",
    "             y=np.arange(len(feature_imp_vis_RF.index)) - 0.2, \n",
    "             xerr=1.96*feature_imp_vis_RF[\"diff_std\"], linestyle=\"\", label=\"Random forest\", linewidth=3)\n",
    "plt.scatter(x=feature_imp_vis_RF['mean_diff'], y=np.arange(len(feature_imp_vis_RF.index)) - 0.2, s=150)\n",
    "\n",
    "\n",
    "plt.axvline(x=0, linestyle=\"--\", color=\"black\")\n",
    "\n",
    "\n",
    "plt.legend(loc=\"lower right\")\n",
    "# plt.xlim(-0.1, 0.6)\n",
    "plt.tick_params(labelsize=25)\n",
    "plt.tick_params(axis=\"y\", rotation=30)\n",
    "plt.xlabel(\"% decrease in $Q^2$\", fontsize=25)\n",
    "\n",
    "plt.yticks(labels=[\"Cognitive style\\ndiversity\", \"Social\\nperceptiveness\", \"Skill\\ndiversity\", \"Skill\"], ticks=range(4))\n",
    "plt.xticks(ticks=[0,200,400,600])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true"
   },
   "source": [
    "# Figure 5: Group behavior and collective performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dfs = []\n",
    "\n",
    "for outcome in [\"score_zscore\", \"round_duration_zscore\", \"efficiency_zscore\"]:\n",
    "    for complexity in csop2.COMPLEXITIES:\n",
    "        model = smf.ols(\"{} ~ n_chats_round_zscore + turn_taking_index_zscore\".format(outcome), \n",
    "                       data = df.query(\"complexity == '{}'\".format(complexity))).fit()\n",
    "\n",
    "        model_dfs.append(pd.DataFrame({'coef':model.params, 'se':model.bse, 'outcome':outcome, 'complexity':complexity}).reset_index().rename(columns={'index':'independent_variable'}))\n",
    "        \n",
    "    model = smf.mixedlm(\"{} ~ n_chats_round_zscore + turn_taking_index_zscore\".format(outcome), \n",
    "                   data = df, groups=\"game_id\").fit()\n",
    "\n",
    "    model_dfs.append(pd.DataFrame({'coef':model.params, 'se':model.bse, 'outcome':outcome, 'complexity':\"Overall\"}).reset_index().rename(columns={'index':'independent_variable'}))\n",
    "    \n",
    "    \n",
    "    \n",
    "df_regressions = pd.concat(model_dfs,ignore_index=True)\n",
    "df_regressions['err'] = df_regressions['se']*1.96"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 3.4554129663390465 as critical value instead of 1.96 for multiple comparisons correction\n",
      "Using 3.4554129663390465 as critical value instead of 1.96 for multiple comparisons correction\n",
      "Using 3.4554129663390465 as critical value instead of 1.96 for multiple comparisons correction\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outcomes = [\"score_zscore\", \"round_duration_zscore\", \"efficiency_zscore\"]\n",
    "ivs = [\"n_chats_round_zscore\", \"turn_taking_index_zscore\"]\n",
    "\n",
    "fig, axes= csop2.composition_vs_perf_plots(df_regressions,\n",
    "                                           outcomes=outcomes,\n",
    "                                           variable_order=ivs, figsize=(12,5),\n",
    "                                           ind_var_labels=[\"# Chats\\nin round\", \"Turn-taking\\nindex\"],\n",
    "                                           n_multiple_comp_correction=n_corrections,erroralpha=0.5)\n",
    "\n",
    "\n",
    "axes[0].set_xlabel(\"\")\n",
    "axes[1].set_xlabel(\"Coefficient\", fontsize=25)\n",
    "axes[2].set_xlabel(\"\")\n",
    "\n",
    "axes[0].set_title(\"Normalized score\", fontsize=25)\n",
    "axes[1].set_title(\"Duration\", fontsize=25)\n",
    "axes[2].set_title(\"Efficiency\", fontsize=25)\n",
    "\n",
    "axes[0].tick_params(axis=\"y\", rotation=30)\n",
    "plot_marker_alpha(axes, [0,1,2,3,4], 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true"
   },
   "source": [
    "# Figure S6. Task complexity effects on solver duration and human performance and behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\mathregular{log_{10}}$(Solver time [ticks])')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "df_csop_tasks = pd.concat([pd.DataFrame(csop.task_specs['phase_1']),\n",
    "                           pd.DataFrame(csop.task_specs['phase_2'])], ignore_index=True).iloc[[1,3,5,7,8,2,4,9,10,11]].reset_index().rename(columns={\"usedIn\":\"Phase\"})\n",
    "\n",
    "df_csop_tasks['n'] = [len(x) for x in df_csop_tasks['students']]\n",
    "df_csop_tasks['m'] = [len(x) for x in df_csop_tasks['rooms']]\n",
    "df_csop_tasks['q'] = [len(x) for x in df_csop_tasks['constraints']]\n",
    "\n",
    "df_csop_tasks['nmq'] = df_csop_tasks.apply(lambda x: \"[{},{},{}]\".format(x.n, x.m, x.q), axis=1)\n",
    "df_csop_tasks[\"difficulty_num\"] = df_csop_tasks[\"difficulty\"].map({\"Easy\":1, \"Medium\":2, \"Hard\":3, \"Very Hard\":4, \"Super Hard\":5})\n",
    "df_csop_tasks = df_csop_tasks.sort_values([\"difficulty_num\", \"Phase\", \"computeTime\"]).reset_index(drop=True)\n",
    "\n",
    "df_csop_tasks.loc[[0,1,2,3], \"nmq\"] = df_csop_tasks.loc[[0,1,2,3], \"nmq\"] + [\"a\", \"b\", \"c\", \"d\"]\n",
    "df_csop_tasks.loc[[5,6,7], \"nmq\"] = df_csop_tasks.loc[[5,6,7], \"nmq\"] + [\"a\", \"b\", \"c\"]\n",
    "df_csop_tasks['Phase'] = df_csop_tasks['Phase'].map({\"step1\":\"Phase 1\", \"step2\":\"Phase 2\"})\n",
    "\n",
    "\n",
    "df_csop_tasks = df_csop_tasks.query(\"Phase == 'Phase 2'\").reset_index().assign(complexity=csop2.COMPLEXITIES)\n",
    "\n",
    "sns.pointplot(x=df_csop_tasks['complexity'], \n",
    "              y=np.log10(df_csop_tasks['computeTime']), \n",
    "              hue=df_csop_tasks[\"Phase\"], scale=2, legend=None)\n",
    "\n",
    "\n",
    "plt.xticks(rotation=0)\n",
    "plt.xlabel(\"\", fontsize=23)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "\n",
    "plt.ylabel(\"$\\mathregular{log_{10}}$(Solver time [ticks])\", fontsize=23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(20, 5))\n",
    "\n",
    "complexities = ['Very low', 'Low','Moderate','High','Very high']\n",
    "ylabel_dict = {'normalized_score':'Normalized score',\n",
    "               'round_duration':'Duration',\n",
    "               'efficiency':'Efficiency'}\n",
    "\n",
    "base_categories = {'real-group'}\n",
    "\n",
    "for plot_index, col in enumerate(ylabel_dict.keys()):\n",
    "\n",
    "    ax=axes[plot_index]\n",
    "    g = sns.pointplot(x=\"complexity_cat\", y=col,\n",
    "                      ci=95, data=df.query(\"group_formation in @base_categories\"),n_boot=1000,\n",
    "                      scale=3,\n",
    "                      ax=ax)\n",
    "\n",
    "    ax.tick_params(labelsize=tick_size)\n",
    "    ax.set_ylabel(ylabel_dict[col],fontsize=label_size)\n",
    "    ax.set_xlabel('',fontsize=label_size)\n",
    "    ax.set_xticklabels(complexities,rotation=75)\n",
    "\n",
    "    ax.yaxis.set_major_formatter(ticker.ScalarFormatter())\n",
    "\n",
    "    if col == \"normalized_score\":\n",
    "        ax.yaxis.set_major_locator(ticker.MultipleLocator(5))\n",
    "        ax.set_ylim(79,101)\n",
    "    elif col == \"round_duration\":\n",
    "        ax.yaxis.set_major_locator(ticker.MultipleLocator(2))\n",
    "        ax.set_ylim(1.1,10.5)\n",
    "    else: \n",
    "        ax.yaxis.set_major_locator(ticker.MultipleLocator(20))\n",
    "        ax.set_ylim(9,85)\n",
    "        \n",
    "        \n",
    "plt.subplots_adjust(wspace=0.3, hspace=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HUyHCmfnUGa9mgeU75/L92Xw+L9jvcLw3gZvN7Arn3CdhxCEiIiIiIiIiUq1tTkpn3e7Uyg6jQoVT82lj4GMLvyeTgrUL9gXO78t3jkC/LcBWICNfWwe8bYjPCzN+ERERERERERGpwko988k519nM2gNvAO2Ab4F/AzOcc3uD/cysMd4Mp/8DDgU2AOc657aYWX/gGuCPeEmo58zsK+fcrrJ8MSIiIiKRYGaHVtS1nXMzK+raIiIiIpUhnJpP8cCHwADgLufcPX79Aomo94D3zOwu4A7gIzMb5ZxbClxuZouBx4F44Arg3jC+BhEREZFIm8GBZQbKi0MbwoiIiEgNE86yuyuBgcAPoRJPBTnn7gJ+xEtYXZHv/BPAwsCnx4QRi4iIiEhlsQr6EBERiRjn3AFHkYoQzpu1c/Heyr1RynGvA2OA84HH8p1/FzgI6BlGLCIiIiKV4e5i2ocDJwb+vBf4HvgVSAXqAd2BsUATvOeqj4H5FRGoiIhIQVv3pvPa3I2889MWtuxNB2D9njRO+Pd3nD+iA6cf1I4GdWMrOUqpScJJPnUPHHeUclywf/cC59cEjk3CiEVEREQk4pxzIZNPZjYeuAUv0XQz8JxzLsunXxxwCfAAcDTwqnPutYqJWEREBDKyc7nrg2W8OX8TeT4TnZZvS+aO95fx4CcruP6YnlxycBfMNClXyi6cZXd1Asf2pRwX7F+nwPnswDEtjFhEREREqgwz6ws8G/j0WOfck36JJwDnXJZz7ingeCAaeNbMekcoVBERqWVSM3O44Lk5vD7PP/F0QN+sXP7+0XLu/GCZluNJuQgn+bQhcLygpAPMS5UG+28s0NwicNwTRiwiIiIiVcm1QF3gZefcrJIMCPSbCiQA11VgbCIiUks557ju9YXM35BUqnEvzdrAU9+uKb6jSDHCST59hFcM8yAz+48VMwcv0P4fvLpODpheoMtBgeOmMGIRERERqUqOxnve+baU44L9jy7fcEREROCblTv5cvnOsMY+9sVqdqVklnNEUtuEk3x6GAimS68GFpnZZWbWw8yiAcwsOvD5ZcDPwFWB/nsD4wn0M+AEvIe0r0oagJkdYmZvm9k2M8sMHD83sxOLHy0iIiJSYdoGjtlF9ios2L9tkb1ERETCMHXWhuI7hZCVm8eb8zVXRMqm1Mkn59xO4CwgHW8GVD/gKWAFkGVmmUBW4POngP6BfmnAGc65XfkuNxbIBNYCH5bk/mZ2GzATOBT4FHgEbzZVE+Dw0n49IiIiIuUoNXAcVspxwf6qgSkiIuVqy950ZqzaVXzHIrw6Z6NqP0mZhLPbHc65b8xsBPA/vARSfn77MX4PXO6cW17gOt9TePe7kMzsbOBe4Eu8RFZKgXbtBSkiIiKVaRFwJHCJmf3bOVew1mUhZtYRb9c7FxgvIiJSbn7euJey5o227E1nV0omLRvWLZ+gpNYJK/kE4Jz7BTjEzIYCp+G9sWsL1MN767cVmA+875xbUNZAzSwKeBDvjeC4gomnQEylneIuIiIiUp5ewEs+NQC+NbMJgZdtvsxsLPAS0BAv+TQlEkGKiEjtkZxRPr8m70jJUPJJwhZ28ikokFgqc3KpBMYAXYC3gCQzOwlvSV8GMLekO8qIiIiIVBTn3MtmNgE4BuiIl4BagFfb8le8l2gJeDO/j+TA5XlfOOdeiXDIIiJSw9WJCafUc2FnPz2LY/q25pSBbTisVwvqxESXy3Wldihz8imChgeOO4CfgAH5G81sJnBWgZpSIiIiIpH2B2AaENwIZWjgw09w1+CPgHMrNiwRKWjC5DlsTkqnfZN4pl4ysrLDEakQ7ZsklMt1MrLzmL5oK9MXbaVB3RiO69eaUwe1ZUy3ZsREl0+CS2qu6vQ3pGXgeAUQj7cVcQO82U+f4RUgn+Y3MLAb33wzm79rl3JTIiIiUnGcc+nOuZOBC/F2/bUiPhYCFzjnTnHOqdi4SIRtTkpn3e5UNielV3YoIhVmaKcmtGscX67XTMnI4a0Fm7nw+bmM/MdX3PbeEuas3UNenoqSi7/qNPMpOKfP8GY4BQtyLjOz04FVwGFmNrrgEjzn3DPAMwDDhg3TvwYRERGpcM65l4GXzawz3gzutkB9YD9ebcy5zrnw974WEREpgegoY9zIjjz02coKuf6e1Cxenr2Rl2dvpHXDupw8sA2nDGrLwPaNMLPiLyC1QpmST2ZWD6/Y+EigPV6xzOIWfjrn3FFh3C4pcFybL/EUvGC6mX2Gt1PMCED1n0RERKRKcM6tB9ZXchgiIlKLnT+iI099u4b9GTmlHmt4O2KUxPbkDJ77fh3Pfb+OTs0SOGVgW04Z1JZerRuU+r5Ss4SdfDKza4F78Ja+lXgYJf97W1AwTbs3RHswOVW+8wlFREREREREqrF6daJpEh9b6uRTm0Z1mXrxSH7evJfpi7by/a+7yS3h0roNe9L47ze/8t9vfqVnq/q/JaI6N68Xzpcg1VxYyScz+zvwN34vklmU4N/Mss63mwnkAD3MLM45l1WgvX/guL6M9xERERERERGpMR79YhWbSlnbrFuLerwwaQQdmibQvVV9zhranj37M/lk6XamL9rK3PWJuBJOLVm1Yz+PfLGKR75YxcD2jThlYFtOGtiGtuVci0qqrlInn8xsIHALXlJpBXAlMBtID5w7HfgS6AQcD/wf3pK8F4ErnHOZ4QTqnNttZm8A44E7gNvyxXQMcBywD/g0nOuLiIiIlDczawf0BZoAdUsyxjn3UoUGJSIitcq89Yk8M3Ntift3bV6P8aM6MW5ER+LjDqyq06x+HS4Y1YkLRnVi+74MPly8lemLt7Fo094SX3/x5n0s3ryP+z5ezojOTTllUBtOGNCG5vXrlPgaUv2EM/PpisAxGzjOObcJOKCQWGC3luXAcjN7FngLb8eXhsCZZYj3Brz6Urea2aHAXLwk1+lALvBH59zeMlxfREREpMzMbCJwE9CnlEMdoOSTiIiUi9TMHG58c5HvDKVTBrbh5EFt+dvbi0lMy6ZpvTieGj+EEV2alqhQeOtGdbn0kK5cekhXNu5JY/rirUxftJUV21NKHN/c9YnMXZ/InR8sY2z35pwyqC3H9WtNo/jY0nyZUg2Ek3w6FO/B6M1g4qkozrn9ZnYm3m50fzCzM51zb4dxX5xzO81sJN6sp9OBUUAK8BFwv3NudjjXFRERESkvZvY8cFHw08qMRUREard/fLycjYlphc63bliXv/9hAI0SYnngkxUkpmXTKD6WkV2bhXWfjs0SuPqI7lx9RHdW70hh+qKtfLBoK+v3FL63nzwH363ezXerd3Pbu0s5tGcLTh3clqP7tCQhrkz7pEkVEc7/xfaB408h2gvNlXPOpZrZFLzlehcCYSWfAtdKxJsBdUO41xARERGpCGZ2HjAx36kfgK+BLUBYpQdERETCMWPlTl6Zs9G37cGzBtIooWJmF/Vo1YAbju3F9cf0ZOmWZKYv3sqHi7aydV9GicZn5ebx5fIdfLl8B/Gx0RzVpyWnDGrL4b1aUCcmuvgLSJUUTvIpIXDcUuB8Gt5Oc41CjFsaOA4O454iIiIi1cGlgWMWcJ5z7r1KjEVERGqpfWnZ3Pz2Yt+28SM7cljPFhUeg5kxoH0jBrRvxF+P781PG5P4YNFWPl6yjd37C+4f5i89O5cPF2/jw8XbaFA3huP6teaUQW0Z060ZsdFRFfwVSHkKJ/mUjFc0s2CaNAkv+dQtxLgGgWPLMO4pIiIiUh0MxitPMEWJJxERqSx3fLCUHcmFJ9x2apbALSeWthxh2UVFGcM6N2VY56bccXJfZq9NZPqirXyydBvJGTklukZKRg5vLdjMWws207ReHCcOaM0pA9syvHNToqK0yr2qCydV+Gvg2K7A+V/w6hocHWLcwYFjyRZ9ioiIiFQ/wRniMyvj5mbW3syeN7OtZpZpZuvN7DEzaxLGtQ4xs7fNbFvgWtvM7HMzO7FAvx5mdrOZfW1mm8wsy8x2mNn7ZnZEiGtPNDNXxMcVfuNERKR4Hy3exvs/by103gweOXsQ9epUbg2lmOgoDu7RnAfPGsi8247muQuHcdrgtiTElXxJXWJqFi/P3si5z8xmzANfc++Hv7Bo016cX2V1qRLC+Vs3HxgBHFTg/OfAMcAQM7vYOfd8sMHMTgPG470JXBhmrCIiIiJV3VagC5VQaNzMugE/4s0yfx9YgffMdh1wvJmNdc7tKeG1bgPuBXYDHwLbgOZ4z3+HAx/n634vcC7ei8iPgUSgF3AqcKqZXeec+0+IW70P/Oxzfn5J4hQRkQPtTMngtveW+LZddmhXhnVuGuGIilYnJpqj+7bi6L6tSM/K5asVO5i+aCvfrNxFVk5eia6xPTmDyd+vY/L36+jYNIFTBrXh1EHt6NW6QfGDJWLCST59CVwFHGNmUc654N+Il/B2oWsIPGtmlwNr8JbhDcN7CHPAs2WOWkREpBZKz8rlk6XbWLx5H7tSvKn0+9Ky2b4vg9aN6lZydBLwFV7dp8HAqxG+95N4iadrnXOPB0+a2aPA9cB9QLEziszsbLyE0pfAGc65lALtBUsvfAo86JxbWKDfYcAXwENmNs05t83ndu85514oLiYRESmec46/vb2EpLTsQm29WjXghmN6VkJUJRcfF83JA9ty8sC2JGdk88WyHXywaCvf/7qb3LySzWjamJjGE9+s4Ylv1tCzVX1OGdiWUwa1pXPzehUcvRQnnOTTp8AGoC7eErvPAZxzuwJTpF/BW843LPABv7/9e8U590aZIhYREalldu/P5KkZa5g2f1OhugiJaVmMffBrjunTiquO6MbA9o0rJ0gJegxvZ9+LzezBks40Kisz6wocC6wHnijQfCdwGTDBzG50zqUWcZ0o4EG8MgnjCiaeAJxz2QU+f8HvWs65b81sBt7M+DGUYbdjEREp3rT5m/lqxc5C52OjjUfPHVStdoprWDeWM4e258yh7UlMzeKTpduYvmgrc9YlUtKVdat27OeRL1bxyBerGNCuEacOastJA9vQtnF8xQYvvkqdfHLOZeBNJ/dre8PMtgF34dV4Cl5/NfC4c+6/YcYpIiJSK/2yNZlJL8z1LRoalJvn+HTZdr5YvoN/nN6fc4d3jGCEkp9zbrmZXQk8B3xmZmc559ZH4NZHBo6f55uVHowpxcx+wEtOjcKbnRXKGLznvLeAJDM7CegPZABznXOzShlXMFEVqprsYDP7P7yXmluAb5xzm0t5DxGRWm9TYhp3T1/m23bdUT3o1zbUpvRVX9N6cYwf2YnxIzuxfV8GHy3xElE/b9pb4mss2bKPJVv2cd/HyxneuQmnDGrLiQPa0Lx+nYoLXA5Q7pXGnHMzgSMDU7KbAWnOueTyvo+IiEhNt2bXfsY9N5u9PtPn/eTmOW5+ewkxUVGcObR9BUcnfszsQiAPmIZXB2mFmX0IzMarn1RsAQvn3Eth3LpX4LgqRPtqvORTT4pOPg0PHHcAPwED8jea2UzgLOfcruICMrNOwFF4s6hCFWC/rsDnuWb2HPB/gReeIiJSjLw8x03TFpGalVuobXCHxlxxWKgN6auf1o3qcsnBXbjk4C5s3JPG9MVbmb5oKyu2F5qoG9K89UnMW5/EXR8sY2z35pwysC3H9WtNo4SCq8qlPFVYmfvAlOztFXV9ERGRmiwvz/GnVxeWOPGU39/eWcKwzk3o1Ez1DSrBC3g1Lgkc44DTAx8l4fDqaJZW8JX2vhDtwfONi7lOy8DxCmAdXomFOUAn4BHgOLzE2uFFXcTM6uCVYqgD/MU5l1SgyzrgGrzyDZsD8R8M3A9cjldDdFwxsYqICDDlx/XMWZdY6Hzd2CgePWcQMdHhbHJf9XVslsDVR3Tn6iO6s3pHCtMXezOi1u0Oubr8AHkOvlu9m+9W7+bW95ZwWM+WnDKoDUf3aVXpOwLWRPovKiIiUgX9sGY3y7eFN3E4KzePl2Zt4PaT+5ZzVFJCBXe6i/jOdz6CMRRXKSNYEMTwZjgtCny+zMxOx5tZdZiZjQ61BM/MooGpwFjgDeDhgn2cc98C3+Y7lQZMM7PZwCLg/EDNrEUFxwbucRleHSs6dtQyUxGpvX7dmcKDn67wbfvr8b3p2qJ+hCOqHD1aNeCGYxpw/dE9WLY1memLvBlRW/eVbBJtdq7jy+U7+HL5DuJjozmqT0tOGdSWw3q2oG5s9amVVZVVSPIp8LbrEn6v+7QIeKYkU7RFREQEXp69oUzjp83fxE3H9iI+Tg9METapku4bnNkUqqhHwwL9QgnOUFpbMPHjnEs3s8/wnvFGAIWST4HE08vA2cCbwAXOlbQ0LDjnNpnZx8B44FC8Z0i/fs8AzwAMGzasxNcXEalJsnPzuOHNRWTlFF7RPbZ7My4c3TnyQVUyM6N/u0b0b9eIm4/vzU8bk5i+aCsfLdnG7v1ZJbpGenYuHy7exoeLt9GgTgzH9mvNqYPbMqZbM2Jr6CyySCh18snM+uI9VDjgaufc7ALtDfDeZA3Kd/pM4FozOzbUGywRERHxZObk8rXPbjWlkZyRw6y1uzmyd6tyikpKwjn3YiXdemXgGGof7R6BY6iaUAWvszdEezA5VWirIDOLAV7FSzy9ClzonCtcgKR4wZeVWjcqIlKEJ79Zw+LNhd8pNKgTw0NnDSIqqipMvK08UVHGsM5NGda5Kbef3Jc56xL54OetfLJ0W6Hdg0NJyczh7Z828/ZPm2laL44T+rfmlEFtGdG5aa3/71ta4cx8OgkYjFeIco5P+/2B9oJaAO+YWV/nXOgte0RERGq5PfuzyM4t+2SO7fv047YW+SZwPNbMovLveBd4MTgWSMcrfF6UmXg70/UwszjnXMHXxP0Dx/X5T5pZHN5Mp9PwalZNKrjrXimMDBzXhjleRKTGW7J5H49/vdq37c5T+9G2caF3BLVaTHQUY7s3Z2z35tz7h/58t3oX0xdt5fNfdpDmU6jdT2JqFq/M2cgrczbSqmEdTh7YllMGtWVQ+0aYKRFVnHDmjB2NN+vp84LTqM2sId5UbIdXSPJkvIeUJwNdOgMTwg1WRESkNsjNK59VRDl54f7uL9WNc24NXvHuzsDVBZrvxptF9JJzLhXAzGLNrLeZdStwnd14dZoaAXfkbzOzY/AKju8DPs13vg7wLl7iaTIlSDyZ2SE+58zM/gaMxtsZ8NNCA0VEhIzsXG5482dyfJ4XjunbijOHtKuEqKqPuJgojurTisfOO4gFtx3DE+OGcHy/1sTFlDw9siM5k8nfr+MPT/zAYQ/N4KHPVrBiezKlWGkOQFJqFlNnb2DPfu+F4Z79mby9YDNpWSWbmVWdhDPzqVPguNCn7US8XU0ccHGgmCTAn8xsDN5SvNOA58K4r4iISK3QuJy2+m2SEFcu15Fq4yrgR+A/ZnYUsBxvFtEReMvtbs3Xt12gfQNewiq/GwLjbjWzQ4G5eM9/pwO5wB+dc3vz9X8a7xlwN7AFuMPnDfAM59yMfJ/PNLNVwLzAmEZ4s7P64xUfH++cC6/ivohIDffI5ytZvXN/ofPN6sVx/xkDNAunFOLjojlpYBtOGtiGlIxsPl+2g+mLt/L96t2+yT0/GxPTeOKbNTzxzRp6tKzPKYO8GVFdmodePf7rzv08NWMN0xdvPaBmV3JGDjdOW8Rd05dx1tD2XHFYN1o1rFvmr7MqCCf51Dxw3ObTdnjguDlf4iloGt5yvIFh3FNERKTWaFA3lv7tGrJ0S/i/e0cZDO/ctByjkqrOObfGzIYB9wDH4yWEtgH/Ae52zhXeh9v/OjvNbCRwG17CaRSQAnwE3F+w3ifQJXBsToHZUgXMyPfnh/GKlh8JNAXygI3AE8CjzjktuRMR8TFn7R6e+36db9t9pw+gef06EY6o5mhQN5Yzh7bnzKHtSUzN4tOl2/lg0RbmrEukpBOaVu/cz6NfrOLRL1YxoF0jThnUhpMHtj1gGeSnS7dz3esLyfQpFB+UkpHDlB/W88HPW3nuomEc1LFJWb+8ShdO8im4W0q2T9sYvFlPX/m0bQocW4RxTxERkVpl/MhO/O2dJWGPP7pPK1o3qhlvyqoiMwsWiHDOuRif8+E64HphDN5ECXbcc86tB0K+Gg8kqm4IfBR3rcNLHuFvY/5c2jEiIrXd/kxvVoxfIuSMg9pxfP/WkQ+qhmpaL45xIzsybmRHdiRn8NHibUxfvJWFG/eW+BpLtuxjyZZ9/OPjFQzv3IRTBrWlYd1Ybpy2qMQlFvakZnHBc3OYdsUY+rZtWPyAKiych5tUvARUy/wnzawZ0C/w6Q8+44JVT7U3oYiISDF2JGeUafxFYzqXTyASSqjEjdY6iIhIhbjvo1/YnJRe6HybRnW589R+PiOkPLRqWJeLD+7CxQd3YVNiGtMXb2X6om0s31byGerz1icxb31S8R19pGblcuUrC/jyhsOIja6+6ZRwkk9rgIOAQ/HW+Af9Ae+By+GffAomqwrvBSkiIiK/efrbNTz2pf8ONiVx5pD2jO3evPiOUhYz8Z55SnpeREQkbN+s2Mlrczf5tj101iAaxZdPvUgpWoemCVx1eHeuOrw7v+5M4YNF25i+aCvrdqdW6H037Enj82U7OGlgmwq9T0UKJ/k0AxgCnGlmZzrn3jazDsAtgfb1zrkVPuOCtZ60hl9ERCSEZ2au4YFP/H6MlsyxfVtx/xkDyjEi8RNqqVk4S9BERESKkpSaxV/eXuzbduHoThzcQy+cKkP3lg244ZgGXH90D5ZtTWb64q18uGgbW/YWnp1WHl6evaHWJZ+eBv4ExAJvmtk+oAHecjqHVyjSz9GB9jlh3FNERKTGe+67tfzjY//EU3BqcSgN6sRw8cFduPaoHkRHaeWXiIhITXH7+0vZlZJZ6HyX5vX46wm9KyEiyc/M6N+uEf3bNeLm43qzcFMS0xdt48PF29i9v/D/t3DNWruHxNQsmtarnrsZlzr55Jz71cz+hJeEigIa52v+Bm9HlQMEdkzpgvfc/HVYkYqIiNRgk79fx98/Wu7bVi8umimThpOencfrczeyePM+tu5LxzmoExPFXaf249RBbalXJ+w61SIiIlIFTV+0lQ8XF95oPsrgkXMGkRCnn/1VSVSUMbRTU4Z2asptJ/VhzrpEpi/aygeLtpKWVdY9SbyaoLUm+QTgnHvOzOYDlwDdgTTgC2Cycy7HZ8jZwAa8bXRnhBeqiIhIzTTlh3Xc++Evvm0JcdG8cPEIhnduCsBhPb1NY494eAbrdqfStnE854/oGLFYRUREJDJ2JGdw+/tLfduuOKwbQzo2iXBEUhox0VGM7d6csd2bM7xzE26c5r90sjRycqtvWcmybOX7M3BNCfveBNwU7r1ERERqqhd/XM/d04tIPE36PfEkIiIitYNzjpvfXszetOxCbb1bN+C6o3tUQlQSrhYN6pbLdRonVN/C8tV3nz4REZFqbuqs9dz5wTLftvjYaJ6fOJwRXZR4EhERqW1en7eJGSt3FTofG23869zB1ImJroSoJFyDOjSmTkzZ0i/tGsfTrnF8OUUUeRWafDKzOmZWryLvISIiUh29PHsDt7/vn3iqGxvF8xOHM6prswhHJSIiIpVt4540/h5iOf71x/SkT5uGEY5IyqpRfCynDGpbpmuMG9mRqGq8qUypk09mFm1mAwMfvotMzexQM5uLVwsq2cxWm9nEMsYqIiJSI7w6ZyO3vedfw6FOTBTPXzSc0d2UeBIREaltcvMcN01bRKpPceohHRtz+aHdKiEqKQ8Xju4U9ti4mCjOGdahHKOJvHBmPh0D/AwsBFoVbDSz4XjFx4fi7QxtQDdgspmp7pOIiNRqr8/dyC3vLvFtqxMTxeSLhjOme/MIRyUiIiJVwfPfr2Pu+sRC5+Njo3nknMFEV+OZL7XdwPaNmTimc1hjbz2xDy0a1CnfgCIsnOTT8YHjz865FT7t/wJi8ZJO24H5QG7g87+bWddwAhUREanu3py3ib++4594iouJ4rmLhnFwDyWeREREaqNVO1J46POVvm23nNibLs1V0aa6u/3kvpw2uHTL7649sjsXhZm0qkrCST6NBhzwTcEGM+sNjAm0vwJ0cM6NAA4BsvCSUpeGHa2IiEg1NW3+Jm5+x3+L3biYKJ69cBiH9GgR4ahERESkKsjOzeOGN38mKyevUNshPZpzwajwl2xJ1REdZfzrnMH85fheNKgbU2Tflg3q8MjZg7jh2F4Riq5iFf3V+msZOC73aTshcMwD/uKcywVwzs02s/eAc4AjwriniIhItfX2gs385e3FOFe4LS46imcmDOWwnko8iYiI1FaPf/0rS7ckFzrfoG4M/zxrIGZabldTREUZVx3enYljOjN90VbeXrCF+RsSyXMQbcbBPZpz3vAOHN23FbHRFbpHXESF85UE1wMk+bQdEjjOc85tK9D2XeDYI4x7ioiIVEvvLtzMTW8tCpl4+t+EoRzeq2XhRhEREakVFm3ayxPf/Orbds9p/WjTKD7CEUkkJMTFcO7wjrx5xWg6NfOWVHZslsCLF4/ghAFtalTiCcJLPsUWOOYXXHL3rU/bzsCxQRj3BMDM1puZC/GxPdzrioiIVIT3Fm7hxjf9E0+x0cZTFwzhiN5KPImIiNRWGdm53PDmz+TmFX5YOL5fa/4wuF0lRCVS/sJZdpeIt8vdAYXDzWwA3pI8B8zyGRcszZ4dxj3z2wc85nN+fxmvKyIiUm7e/3kLN7z5Mz7Pkl7iafxQjupTaNNYqebMrGOYQ/OAFCDZOb90pYiI1ET//HQla3alFjrfvH4c953eX8vtpMYIJ/m0FGgNnGVmD+R7QLoocMzj9yV2+XUIHHeEcc/89jrn7irjNURERCrM9EVbuf4N/8RTTJTxxLghHN1Xiacaaj3ei7hw5ZrZMryNXZ51zvnV2BQRkRpg1po9PP/DOt+2f5w+gGb16/i2iVRH4Sy7ey9wHAy8a2anmdktwLUEdsFzzvnVgxoeOK4I454iIiLVwkeLt/F/RSSe/jtuCMf2ax35wCSSrAwfMcBA4DpgsZk9EOngRUSk4qVkZHPTtEW+bWcNbR/RZ4X2TeLp0rwe7ZuotpRUnHBmPk0Grge6AacEPsB7YMoD7i04wMzqAsfgJadmhhXp7+qY2QVARyAVWAzMDO6sJyIiUlk+WbKNa19f6Fu3ITrKePz8gzi+vxJPNdyLgWMH4MjAn3OAX4C1eM8u9fDKF/TFexZzwNfAdqAZMBRoAUQDfzazWOfcjZH6AkREpOLd++EvbNmbXuh8u8bx3HFK34jGMvWSkRG9n9ROpU4+Oecyzew44C282U9B6cD1zjm/JXfn4T1oBR+uyqI1MLXAuXVmNsk551foXEREpMJ9unQb17wWOvH0n/MO4oQBbSohMokk59wkMzsCmAZkAg8ATzjndhfsa2bNgT8BN+M9U53tnJthXoGP84D/Ak2A68zsBefckgh9GSI1XlZOHrPW7iE53StHm5KRza87U+jeMuy9kURK7MtfdvDm/M2+bQ+dNZCGdf329hKp3sKZ+YRzbi0wxMyGAt2BNOD7EMvtALKAu/FmRs0P554BU/DqSS3DK8rZFe+h7TLgEzMb7ZwrNHfRzC4L9KFjx3DrgIqIiPj7bNl2/vTqQnJCJJ7+fd5gThqoxFNtYGYd8F7QNQROdM59EapvICF1l5nNAj4CppnZQc65zcBrZrYRb8a4AX/EK3EgImWwMzmDqbM38NrcTezen/nb+d37szj60ZmM6NyUC0Z34qQBbYiOUqFnKX+JqVn89R3/dwkTx3RmTPfmEY5IJDLCSj4FOecWAAtK0O/Vstwn33XuLnBqKXCFme0HbgTuAk73GfcM8AzAsGHDtIOMiIiUmy9+2cHVr/zkm3iKMvjXuYM5eWDbSohMKsk1eLOVXisq8ZSfc+4zM5sGnBsYf3Pg/A9m9glwInBoBcUrUmt8t3oXV738EymZOSH7zF2fyNz1ibw2ZyNPTxhKo3jNQJHy45zjtveWHJD4DOravB43H9+7EqISiYxwCo5XRU8HjnowExGRiPlq+Q6uemVBkYmnUwcp8VTLnIxXZuDLUo77PHA8pcD54HU6ICJhm7lqF5OmzCsy8ZTfrLV7GP/cbFJL2F+kJD5YtJWPl2wvdD46ynj03MHEx0VXQlQikVEuySczizWzXmY2yswqIwG0M3CsVwn3FhGRWujrFTu48uWfyM4tnHgyg0fOGcRpg9tVQmRSyYJJotRSjksrMD5oS+BYP+yIRGq5bfvSuSrEDNWiLN2SzC3vqtSalI/t+zK4/b2lvm1XHd6NwR0aRzYgkQgrU/LJzI41s0+BfXi7uPyAT0FxM7vSzJ4xszvLcr8ijA4c11bQ9UVERH7zzcqdXDH1J7Jy8wq1mcHDZw3i9IPaV0JkUgVkB479SjkuuLVRwWkWwWe1fWFHJFLLvfDjevaHOYPpg0Vb2bCntLlkkQM55/jL24tJzij897Bf24Zcc2SPSohKJLLCSj6ZWYyZPQ98AhwD1MUrhhn8KGgrcClwh5l1DvOe/cysqc/5Tni7wQC8HM61RURESmrGyp1cPnVByMTTQ2cN4syhSjzVYivxnoUuNrMSbZtlZg2BS/CW660o0Bz8y7Sn3CIUqUUysnN5c96msMc7B6/M2ViOEUlt9MqcjcxctavQ+bjoKB49ZzBxMTWlGo5IaOH+LX8KmIj3cJUMvAa8U0T/D4HEwJ9PC/OeZwNbzewTM3vSzB40s7fwHtK6Ax8DD4d5bRERkWLNXLWLy6YuICvHP/H04JkDOUuJp9rujcCxLfBpYPe7kALtnwb6A7xeoMtIvKTUqvIMUqS2mLlqF0lp2cV3LMI7P23BOe1ZJOHZsCeVf3y83LftxmN70qt1id5TiFR7pd7tzszG8vvbuc+B851ze83sNOAMvzHOuVwz+xw4D68o+L/DiPUboBdwEN4yu3rAXuB7YCow1emngoiIVJDvV+/mjy/N9008ATxwxgDOGaaa0MITeLO9+wKjgBVm9j7ec8xavNpOCUBX4Ai8l3J1A2N/AZ4MXsjMEoDjAp9+FongRWqaDXvSiu9UjN37M8nIzlMxaCm13DzHjW8uIi0rt1Db8M5NuPSQrpUQlUjlKHXyCe+BCmAzcKZzrqTf0X/CSz6VtgYCAM65b4FvwxkrIiJSFj/8uptLXpxHZojE0/1nDODc4R0jHJVURc65bDM7DvgC6A3EA+cGPvwEyxUsB45zzuWfotEHeDPw5w8qIFyRGi8ju/Av/eFIz85V8klK7dnv1jJ/Q1Kh8wlx0Tx89iCio/wq1ojUTOEknw7Bm/X0QikSTwDbAsc2YdxTRESkUvy4pujE032n9+f8EUo8ye+cc1vMbAhwK3AF0KyI7nvwyhn8wzmXUeA6C4A/VligIrVAg7rh/LpTcdeR2mPF9mQe/dx/xfStJ/WhUzNt1C61SzjfRYPJo2WlHBd8oKpbZC8RkWJMmDyHzUnptG8Sz9RLRlZ2OFKDzVqzh4tfmEdGtn/i6d4/9Gf8yE4Rjkqqg0Ai6XYzuxc4GBiGV9epHpCKtxnLfOB751xWpQUqUsMNKoft6/u0aUhstApCS8ll5eRxwxuLfDcnOaxnC8bppZXUQuEkn4JzV0v7Hbh54KitgkWkTDYnpbNut7Y9loo1Z23Riad7TuvHhFFKPEnRAomlrwMfIhJhgzs0pnOzBNaXofbTkb1alGNEUhv856vV/LItudD5hnVjePDMgZhpuZ3UPuGk8LcHjj1LOW504Ki9SkVEpEqbuy6RSS/MIz1ErZC7TunLhaM7RzYoEREptfkbkti+L6P4jkV44cf1zFy1q5wikppu4cYknpzxq2/bvX/oT+tGWggktVM4yacf8Ipjnl3SAWbWEjgLr1aUioaLiEiVNX99IpOmzPXdmQbgjpP7MnFslwhHJSIipfXNip1MmDyHjBA1+0oqNSuXSS/M4/W5eocuRUvPyuXGNxeR57MH+0kD2nDqoLaRD0qkighn2d3rwEVAXzP7m3Pu/qI6m1k88BretsIOeCmMe4qIiFS4BRsSuej5uaSGSDzddlIfLj5YiScpOTOLAroBTShh3Uvn3MwKDUqkFnj/5y3c+OYicvyyAGHIzXP89Z0lbExM46ZjexGlXcrEx4OfrmCtT2mI5vXrcO8f+mu5ndRqpU4+Oec+M7NvgCOAv5tZF+CRgv3MrD5wCnAH3hI9B7zhnFtUtpBFRETK308bk7jo+XkhE0+3ntiHSw/pGuGopLoys8OBm4AjgTqlGOoI7+WgiAS8NGs9d36wDBdm3ik6CnzqRAPw5Iw1bEpK56GzBlI3Njr8IKXG+eHX3bzw43rftgfPHEDTenGRDUikign34eZcYDbQFbgk8PHbTi1mtgboAAS/IxuwGG0XLCIiVdDCjUlcNHku+zNzfNv/dkJv/nioEk9SMmZ2J97LN/CegUQkApxz/OerX/nXl/7b2wP89YTejO3WnJdnb+DDxVsPeOHQtUU9xo3oyNlDO/DOws3c8+Evvgms6Yu2sn1fOs9MGEYTJRQESM7I5s/T/OdYnDusA0f1aRXhiESqnrCST8653WY2HHgeOC1wug7e2zqAzhz4sPU2cLFzTttTiYhIlbJo014unDyXlBCJp5uP783lh3WLcFRSXZnZccCd+U5txKt3uQXIrJSgRGqBvDzHPR/+EnLmSZTBP04fwHmBLe4fPGsg953enyMf+ZaNiWl0aprA1zce/lv/SWO70K5xPNe+vtB319N565M446kfmTJxOJ2b16uIL0mqkXum/8JWn8L27RrHc9vJfSohIpGqJ+xp3c65JOB0MzsImAAcgpd0agTsx3vI+hZ4yTk3t+yhioiIlK/Fm/dyweQ5IRNPfz6uF1cersSTlMqfAsc8vGV3/3Yu3MU/IlIS2bl5/OWtxby7cItve1x0FP8+bzAnDGhzwPmY6CiiA7Wb/Go4HduvNW9cNppLXpzP7v2Fc8frdqdyxlM/8uyFwxjaqUk5fCVSHX2+bDtvLdjs2/bw2YNoUDc2whGJVE3h7HZ3AOfcQufcDc654c65Fs65OOdcU+fcAOfcn5R4EhGRqmjpln1c8NwcUjL8E083HduTq4/oHuGopAYYwe91Lh+LdOLJzNqb2fNmttXMMs1svZk9Zmal/s3YzA4xs7fNbFvgWtvM7HMzOzFE/zFm9rGZJZpZmpktNrP/M7OQhXHM7CIzm2tm+81sn5nNMLOTSxur1F4Z2blcMXVByMRTQlw0z08cXijxVFKDOjTm3avG0L1lfd/2xNQszn92Nh8t3hbW9aV627M/k1veXeLbdsnBXRjdrVmEIxKpusqcfBIREalulm7Zx/jn5pAcIvF0/dE9+dORPSIcldQQjQLHTyN9YzPrBiwAJgFzgX8Ba4HrgFlmVuLfgszsNmAmcCje1/IIMB1v177Dffqflq//u8ATQFwghtdD3ONh4AWgDfAs8DIwAJhuZn/yGyOSX3JGNhdOnstXK3b6tjdOiOWVS0dycI/mZbpPh6YJvH3lGMaESCRk5eRx9as/8b9v16CJjrWHc45b3l3C7v1Zhdq6t6zPn4/rVQlRiVRd2k1FRERqlWVb93HB5DnsS8/2bb/uqB5cd7QSTxK27XibrhT+baTiPQm0BK51zj0ePGlmjwLXA/cBVxR3ETM7G7gX+BI4wzmXUqA9tsDnDfGSR7nA4c65+YHztwNfA2eZ2XnOudfzjRkD3AisAYYHyjlgZg/hJdAeNrMPnXPrS/VfQGqNXSmZXPT8XH7Zluzb3qphHaZeMpKerRqUy/0axcfywqQR/O2dJbz9k/8Sq/s/WcHGxDTuPrUfMdF6x1/TvbtwC58t21HofHSU8eg5g7QbokgB+q4oIiK1xi9bkxn/3Bz2pvknnq49sjv/p8STlM0PgWO/SN7UzLoCxwLr8WYd5XcnkApMMLMiKyObWRTwIJAGjCuYeAJwzhX8B3QW0AJ4PZh4CvTLAG4LfHplgTHBJNh9wcRTYEww/jp4M7hECtmUmMY5/5sVMvHUuVkCb10xptwST0FxMVE8fPZArj+6Z8g+r8zZyKUvzQ+5e6rUDFv3pnPnB8t82/50RHcGtm8c2YBEqoGwk09mFmtmZ5nZs2b2g5n9YmZrS/Cxpjy/ABERkZJYsT2Z8c/NDpl4+tMR3bn+mJ6YFS46K1IKj+PVfLrIzOIjeN8jA8fPnXMHbM0VSCD9ACQAo4q5zhigC/AxkGRmJ5nZzWZ2nZmNLubefksNZ+IlssaYWZ0SjvmkQB+R36zekcLZT89i3W7/TbT7tmnItCvG0KFpQoXc38y47ugePHrOIGKj/X9ezFi5i3OensV2n93PpPpzznHz24t9a0YOaNeIPx2pepEifsJadmdmI4BXgK4Fm0owXAuhRUQkolZuT2Hcs3NICpF4uurwbtx4rBJPUnbOudlmdgfwd+CdwHKzfRG4dbC4yKoQ7avxZkb1BL4q4jrDA8cdwE94NZh+Y2YzgbOcc7tKcm/nXI6ZrcObCdYVWB6YfdUO2O+c86vSvDpwDD29RGqlnzftZeKUuSFfIozo3JTnJg6jYQR2FztjSHvaNIrn8qnzfesH/rItmT888QNTJg2nT5uGFR6PRM7Lszfw3erdhc7HxUQFkpJaXCTip9TJp0Axyy+BevyebMoG9gCF9yAVERGpRKt2pDDu2dkkpvqX4Ln8sK78+bheSjxJuTCzQ4HvgdeA84HVZvYSMBvYDeQVMRwA59zMMG4dLHQeKtEVPN+4mOu0DByvANYBRwNzgE54RcePA6ZxYNHx0t67vGKVWuT71bu5bOp80rJyfduP7N2SJ8YNIT4ucnV2RndrxjtXjWHilHlsTkov1L49OYOzn57FE+OHcFjPFhGLSyrOut2p3Pfxct+2vxzXix7lvNRTpCYJZ+bTHUB9vBlMXwB3AXOdc/4/CURERCrJ6kDiaU+IxNNlh3blr8f3VuJJytMMfp/l7YDmeMW+S8pRMRvCBP+SFzcDPfibu+HNcFoU+HyZmZ2ON7vpMDMb7ZybVc73LihkfzO7DLgMoGPHjqW8rFQ3nyzZxnWv/0xWrn/u9vSD2vHPswZWyoyT7i0b8O5VY7n0xXks2lw4n7o/M4eLX5jH3//Qn/NH6O9qdZab57jxzZ/JyC7893BEl6ZcPLZLJUQlUn2E8x36aLyHgR+A451zs5R4EhGRqubXnfs5/9k5vlsgA1x6cBf+doIST1IhLN9Hwc9L8hGO4G+9jUK0NyzQL5Rg8e+1+RJPADjn0oHPAp+OKMO9i+tf3MwonHPPOOeGOeeGtWihGSU12etzN3L1qz+FTDxNHNOZR86u3KVOLRrU4fXLRnNs31a+7bl5jr+9s4R/frqCvDxVIKmu/jdzDT9t3FvofL24aB45exBRUXqeEClKOG/WmgaOU51z+u4pIlILTZg8h81J6bRvEs/US0ZWdjiFrNm1n/Ofnc3u/f6rwS8e24VbT+qjxJNUhLsr6b4rA8dQdZKC2ziGqglV8Dp7Q7QHk1P5i6mvBIYF7r0gf2czi8ErYJ4DrAVwzqWa2RagnZm18an7VNJYpYZ7+ts1PPDJipDt1x/dk2uP6l4lvpfHx0Xz1AVDue+j5Tz/wzrfPk/OWMPGxDQePnsQdWMjtzxQyu6Xrcn86wv/b0m3ndy3wgrci9Qk4SSftuGt+99bvqGIiEh1sTkpPeROQ5Vt7a79nP/MbHal+CeeJo7pzO0nK/EkFcM5V1nJp28Cx2PNLCr/jndm1gAYC6Tj1Z4qyky8RFEPM4tzzhWcOtg/cFyf79zXwHjgeLxaV/kdirfL3kznXGaBMRMCY6YUGHNCvj5SCznneODTFfzv27Uh+9x9aj8uGtM5ckGVQHSUcccpfenYNJ57PvwFv0lOHy7exvZ9GTx74TCa1IuLfJBSapk5udzw5s9k5xb+H3pErxacN7xDJUQlUv2EMz91XuCoHUhERKRKWbc7lfOfnc3OEImni0Z34s5T+irxJDWOc24N8DnQGbi6QPPdeBvFvOScSwUws1gz6x3YSCb/dXYDb+Atfbsjf5uZHYNXcHwf8Gm+prfwiqmfZ2bD8vWvi7frH8BTBWJ6OnC81cya5BsTjD+TwkkpqQWCS9RCJZ5ioozHzh1c5RJP+U0c24X/TRhGfIjZTfM3JHHGUz+yvoq+xJED/fvL1azYnlLofOOEWB48c6CeKURKKJzk0+N49QguMrOK38dURESkBNbvTuX8Z2azI9k/8XTBqI7cdWo/PSRKTXYVsBP4j5m9Z2b3m9nXeAXPVwG35uvbDlgOfOVznRuAX/ESQzPN7GEzmwZ8AuQCf3TO7Q12ds4lA3/EK1Y+w8yeM7N/Aj8Do/GSU2/kv4Fz7kfgUaAbsNjM/mVmTwDz8Uo83OScW1+W/xhS/WTm5PKnV3/i9XmbfNvrxETxzIVD+cNB7SIcWekd07cVb1w+iub16/i2r9udyulP/sCCDYkRjkxKY8GGRJ7+do1v272n9adlw7oRjkik+ip18sk59z1wH9AdeMPM6pV7VCIiIqWwYY8342l7coZv+/iRHbnn1P5KPEmNFpj9NAx4ARgJ3IiX3PkPMNo5t6eE19kZGP8voANwLXAk8BFwiHNums+Y94DD8JbtnQlcA2TjJbLO86sT6py7EZgIbMfbue5CYBlwinPuvyX7qqWmSM3M4ZIX5vPJ0u2+7Q3qxDD1kpEc2du/qHdVNLB9Y967egw9Wtb3bU9Ky+b8Z+fw0eKCZc9qlwmT53DEwzOYMHlOZYdygLSsHG58c5Hv8slTBrXllEFtIx+USDUW1la+zrnbzWwv3lTq1WY2FZgL7AH8t6I4cPzMcO4rIiJS0KbENM5/Zjbb9vknns4f0ZF7T+uvXWikVnDObQImlaDfeorYWc85l4iXOLqhFPf+ATixpP0DY14EXizNGKl5klKzmPjCPBZt2uvb3rx+HV68eDj92obaILHqat8kgbeuHMOVLy/gxzWF879ZOXlc/epPbErqzeWHdq2VL0mqah3JBz5Zwfo9aYXOt2xQh3tP61cJEYlUb2ElnwIWAKvxCk/eVIpxroz3FRERAbzE03nPzGZriMTTecM7cN8flHiS8mdmwYI0zjnXzed8uA64nkhNt31fBhMmz2H1zv2+7e2bxPPyJSPp3Lz6LrZoFB/LC5NGcMu7S3hrwWbfPg98soKNiWncc2o/YqLDqYwi5em71bt4adYG37YHzxxI4wQVixcprbCSQGZ2J78XoXQU8eZMRESkImxOSuP8Z2ezZW+6b/s5w9rzj9MHKPEkFaVz4FhwQUZnyvZs5LPAQ6RmWrc7lQuemxPy+3jPVvV56eKRtG5U/evqxMVE8dBZA+nYNIFHv1jl2+fVORvZkpTOE+OHUL+O3tVXln3p2fx52mLftvNHdOSI3i0jHJFIzVDq72pmdiJwZ75Tq4AfgB14O5OIiIhUqC170zn/2dlsTvL/heWsoe154IyBSjxJRdqIf6Io1HkRyWfpln1MnDKX3fuzfNsHd2jMC5OG16gZJmbGtUf1oEPTeP7y1mKycwt/q/h21S7OfnoWUyYOrxFJt+ro7g+W+daQ7NA0nltP6lMJEYnUDOGk1INr/7OASc6518oxHhERkSJt3ZvO+c/MZlOif+LpjCHtePBMJZ6kYjnnOpfmvIj8bu66RC55YR4pmTm+7Yf0aM7TFwylXg2d/XP6Qe1p3TCey6fOJzmj8H+D5duS+cMTP/D8xOH0bduwEiKsvT5duo13Fm4pdN4MHjl7sGakSYVp3yT+gGNNFM6/ngF4b/QmK/EkIiKRtG2fN+NpY2LhAqAApx/UjofOGkS0Ek8iUg1MmDyHzUnptG8Sz9RLRlZ2OBHx1fIdXPXKT2Tm+O9RdOKA1vzr3MHUiYmOcGSRNbpbM965agwTp8zzncW7PTmDs5/+kScvGMphPVtUQoS1z66UTG55d6lv26UHd2FEl6YRjkhqk9rwMyCcanYJgaN2rBMRkYjZvi+D85+ZzQafnWcAThvclofPrj2Jp/ZN4unSvF6NfkMmUtMFd/kKtYS4pnl34WYum7ogZOLp/BEdePz8ITU+8RTUvWUD3r1qLIM6NPZtT83K5eIX5vHqnI2RDawWcs5xy7tLSEwtvAy0R8v63Hhsr0qISqRmCSf5FPzuF1uegYTDzCaYmQt8XFrZ8YiISMXYkZzBuGdn+255DHDqoLY8UosST+C9IfvmpsNrxZuy6sTMDi7j+MfLKxaRqmTKD+u4/o1F5Ob5l0S78vBu/OP0AbXq+zhAiwZ1eP2PoziuXyvf9tw8Lyny4KcryAvx307K7u2ftvDFLzsKnY+JMv517mDqxtaOhKhIRQon+fQh3g4uZXq4Kisz6wA8DvjvyyoiIjXCzuQMzn92Nmt3p/q2nzywDY+eM0hbU0tV8YGZ9Q9noJn9F7iqnOMRqVTOOf71xSrunv5LyD5/O6E3Nx/fG7PalXgKio+L5snxQ7nk4C4h+zw1Yw3Xvr6QjOzcCEZWO2zZm87dHyzzbbvmyB70b9cowhGJ1EzhPKn/G9gDXBTuw1VZmfeTaUogjqcrIwYREal4O1MCiadd/omnkwa04bFzByvxJFVJY+BTM+tUmkGBGU9KPEmNkpfnuOuDZfz7q9W+7VEG/zxzIJcf1i3CkVU90VHG7Sf35e5T+xFq8teHi7dxwXNzfJeGSXjy8hx/nrbIt/j9oPaNuOoI/d0UKS+lflp3zm0FTsebcfSVmZ1tkX9NcS1wJDAJ8P+NREREqrVdKZmMe3YOa0Iknk7o35rHzlPiSaqcNKAN8LmZlahKcIHEU+jpISLVSHZuHte/+TMvztrg2x4XHcWT44dwzvAOEY6sartoTGeemTCM+BDLvOZvSOKMJ39gXYjZwFI6L81az49r9hQ6XycmikfOGUysnjFEyk2pd7szs+cDf1wMHAG8Duw0s/l4M5H8Kwj+zjnnLintffPdvw/wAPBv59xMMzsy3GuJiEjVtHt/JuOenc2vO/1XVh/XrxX/Of8gPRRKVXQO8B7QHfjEzA53zoUsEWBm/wGuDnz6C3BUhUcoUsHSs3K5+tWf+HrFTt/2enHRPHPhMMZ2bx7hyKqHo/u24s3LR3Pxi/PYlZJZqH39njTOePIHnr1wGMM6awe2cK3ZtZ8HPl3h2/aX43vTvWX9CEckUrOVOvkETASC1e6Cx5bAiaW4RljJJzOLAabiFT2/JZxriIhI1bZnfybjn53D6hCJp2P7tuLx84co8SRVknPu48AmKFOAg4D3zewE51yhdTI+iacjnXP+v62LVBP70rO59MV5zFuf5NveJCGWFyaNCLnDm3gGtG/Eu1eN4eIX5rFqR+Gfh0lp2Yx7bg6PnjOIkwe2rYQIq7ec3DxueHMRGdmF502M6tqUSWM6Rz4okRou3Cd3K8NHWdyB9yA30TlXO/akFRGpRRJTsxj/3BxW7kjxbT+6Tyv+O24IcTFKPEnV5Zx7CbgZ77nncODVgiUKzOzfwJ8CfZYDRynxJNXdzpQMzntmdsjEU+uGdZl2xWglnkqofZMEpl0xhrHdm/m2Z+Xk8adXF/L0t2twTjvhlcbT365h0aa9hc7XrxPDw2cPIqqW7booEgnhzHwKvQ1DBTKzEXiznR5xzs0q5djLgMsAOnbsWAHRiUhF25mcwTsLt7B8WzI7kjMASErLYuOeNDo2S6jk6KQ8JKVmMe7Z2azY7p94Oqp3S54cr8STVA/OuYfNrCVwE16tzKeBy+GAxBN4iacjnXOF9/gWqUY2JaZxweQ5bNiT5tvepXk9pl4ygvZN9DO7NBrFxzJl4ghueXcJby3Y7NvngU9WsDExjXtO7ac6iCWwdMs+HvvSvwj+HSf31d9RkQpS6uSTc86/amAFyrfcbhVwe2nHO+eeAZ4BGDZsmF4LiFQjv+5M4V9fruazpdvJyTvwn+/etGwOe/gbDu/ZgmuP6sFBHZtUUpRSVkmBGU+hEk9H9GrBkxco8STVi3PuL4EE1IXApWa2G6gPXBPosgIlnqQGWLk9hQufn8OO5ML1iQD6tW3IixePoHn9OhGOrGaIi4niobMG0rFpAo9+scq3z6tzNrIlKZ0nxg+hfp1w5hfUDpk5udz45qJCz5TgveQ6e1j7SohKpHaoLk/x9YGeQB8gw8xc8AO4M9Dn2cC5xyorSBEpX9+s2Mkpj//AR4u3+T4kADgH36zcxdlPz+LN+ZsiHKGUh71pWVwweQ6/bEv2bT+8VwueumAodWL8d/4RqeIuAT7CW173V36f8bQSJZ6kBvhpYxLn/G9WyMTTiC5Nee2yUUo8lZGZce1RPfjXuYOIjfZfEvbtKu95aNs+VScJ5dEvVvku7W+SEMv9Zw4g8pu4i9Qe1SUtnglMDtE2BK8O1Pd4D3KlWpInIlXT7LV7uHzqArJyi9tA05OT5/jLW4upGxvNqYNUeLO62JeWzQWT57Bsq3/i6dCeLXj6gqHUDbHltEhV55zLNbOzga+A0YHTK4HDlXiS6u671bu4fOoC0rJyfduP7tOS/44bou/h5ej0g9rTplE8l700n+SMnELty7clc/oTP/L8xOH0bduwEiKsuuatT+SZmWt92+47fQAtG9SNcEQitUu1SD4Fiotf6tdmZnfhJZ9edM49F8m4RKRipGflcs1rC0uceMrv5rcWM6pLU1o21ANEVbcvPZsJz89h6Rb/xNMhPZrzzAQlnqRqMrNDSznkIbwd8KKBu4FeZtbLr6NzbmYZwxOpcB8t3sb/vbGQ7Fz/mclnHNSOB88aqJ1JK8Cors1456oxTHphHpsSC89y2p6cwdlP/8gT44dweK+WlRBh1ZOamcONby7Cry77aYPbcuKANpEPSqSWqRbJJxGpXaYv2squFP/p+8VJz87l1bkb+b+je5ZzVFKekjOyuXDyHBZv3ufbfnD35jx74TAlnqQqmwGEW0fylSLaHHo+kyrutbkbueXdJb6/yANMGtuZ20/qqx3DKlD3lg1496qxXPLifN9d21Kzcrnkxfnce1p/xo3Uhkv/+Hg5GxMLF8Nv1bAO95zavxIiEql99CpCRKqcqbPLtq/Ba3M3kh3GrCmJDC/xNJdFIRJPY7o1U+JJqguroA+RKsk5x5MzfuVv74ROPN1wTE/uOFmJp0hoXr8Or/9xFMf1a+XbnpvnuOXdJTzwyQryQtTOrA2+XbWLV+Zs9G3751mDaJQQG+GIRGqnav9mzTl3F3BXJYchIuVk+74MlmzxT0qU1I7kTL5dtYujerdU4cgqJiUjm4uen8vPPm9pAUZ3bcbki4YTH6fEk1R5d1d2ACKR5Jzj/k9WhKyZYwb3nNqPCaM7RzawWi4+Lponxw/l/o+X89z363z7PP3tGjYlpfHI2YNq3YudfWnZ/OWtRb5t40d25LCeLSIckUjtVe2TTyJSs+xMySiX61z64nyaJMTSq3UDerVqQK/WDb0/t26gLYgryf7MHCZOmcfCjXt920d2acrkicOUeJJqwTmn5JPUGjm5edzy7hLenL/Ztz0mynjknEGcNrhdhCMTgOgo47aT+9KxWQJ3fbAMv0lOHy3exo59GTxz4TCa1ouLfJCV5I4PlvruxNipWQK3nNinEiISqb30G5iIVCnlOSs8KS2b2WsTmb028YDz7RrH0zuQiAp+dG1en7gYrUSuKPszc5j4/FwWbEjybR/RpSlTJg0nIU4/lkREqpKM7Fyue30hny3z35yxbmwUT40fyhG9Vdi6sl04ujPtGsfzp1cXkp5deAfC+RuSOOPJH5gyaQRdmterhAgj6+Ml23j/562FzpvBI2cPop5eRopElP7FiUiVEh9T8bNetuxNZ8vedL5asfO3czFRRrcW9enZuoGXmGrlJaXaNY5X3YoySs3M4eIp85gfIvE0vHMTpkxU4klEpKrZn5nDZS/N58c1e3zbG9SN4fmJwxneuWmEI5NQjurTijcvH83FL87z3bxl/Z40znjyB569cBjDavD/t50pGdz67hLftssO7Vqjv3aRqkpP+iJSJeTlOd5ZuIWHPltRKffPyXOs3JHCyh0pTM9XGqBeXPRvCamegYRU79YNa9WU9bJIy8ph0gvzmLs+0bd9aKcmTJk0Qm8fRUSqmMTULCZOmRtyV9Lm9esw9ZIR9GnTMMKRSXEGtG/Ee1ePZdKUuazasb9Qe1JaNuOem8MjZw/ilEFtKyHCiuWc429vLyEpLbtQW69WDbjhGO2ILFIZ9LQvIpXuxzW7ue+j5SzbmlzZoRSSmpXLwo17C9Upal6/zoFL91p5ySnVK/pdWlYOF78wj7nr/BNPQzo25oVJw1WDS0Skitm6N50Jk+ewZleqb3uHpvG8fMlIOjWr+Uu3qqt2jeN568oxXPnyAn74tfDMtaycPK55bSGbk9K54rCuNWqDlmnzNx8wuz0oNtp49NxB1InALHsRKazUT/xmthZwwOXOuS9LMe5Q4AXAOee6lfa+IlLzrN21n/s/WcEXv/jXkQiHAY+ffxCpWTms2J7Cyu0prNqRwu79WeV2D4Dd+zP5/tdMvv919+/3NujUNIGerQJL9wJFzjs3SyAmunbVk0rPyuWSF+YXqrcVdFDHxrx48Qga1NX2xlJzmdkhwARgJNAeaAgU983AOeeUkZVKs2bXfi6cPJcte9N923u1asBLl4ygVcO6EY5MSqth3VimTBzBre8uYdoC/2LxD366go2Jadx7Wr8a8ayyKTGNez78xbftuqN60K9towhHJCJB4TzcdMZLPiWUclx8vrEiUoslpWbx769W8/LsDeSUZ4Vx4MZje3KyzxTy3fszWRlIRq3cnsKKHSms3pFCWlbhgpzhcs6rpbB+Txqf50uoxcVE0b1FfW/pXuvg0r0GtG5Yt0a9aQzKyM7l0pfmMWutf42QQR2UeJKazczqAy8BpwVPVWI4IiW2dMs+Lnp+LntS/V/YHNSxMVMmDqdxgpaeVxdxMVH886yBdGyawCNfrPLt89rcjWzZm84T4w6q1j+b8/IcN01bxP7MnEJtgzs05orDNP9BpDLpzZqIRExmTi5TZ23gP1+tJjmj8INBftFRxpAOjZkXoki1n8sO7crVR3T3bWtevw7Nu9dhbPfmv53Ly3NsTkpnxfZkLym1w0tMrd2dSm45JsWycvL4ZVsyv2w7cFlhw7ox+Xbca/hbkfNG8dX3wS8jO5c/vjTfd4o/wKD2jXjp4hE0rMYPtyIl8CZwHF7SKRVYAozCewH3C5AOdAJaBPo7YEGgr0ilmL12D5e+ON/3F3eAQ3o0538ThmpziGrIzLjmqB50aJrAX95aTFZuXqE+M1ft4uynZzFl0nDaNIqvhCjLbsqP65njs9S/bmwUj5wzqEbM7BKpziL50yP4XazwtgsiUqM55/h06XYe+HQFG/akFdv/qN4t+duJvenesgFfr9jBA5+s8C2YGdS+STzXH92TM4e2L1VcUVFGx2YJdGyWwLH9Wv92PjMnlzU7U1m1IyWwdC+ZVTv2h1yCEK7kjBzmrU9i3voDE2xtGtX9rY5UMDnVrUV96sZW7RoFwcTTd6t3+7YPaNeIly4ZWa2TayLFMbOTgePxEkrTgEucc/vNLPjb3q3OuQ8CfQ8C7sCbIRUPnOecW1sJYUst98UvO7j61Z/IyimclAA4aWAb/nXOYOJi9Mt7dfaHg9rRulFdLp+6gH3phYtxr9iewh+e+IHnJw6vdsvTft25n39+6r9pzV+P7023FvUjHJGIFBTJ5NOowHFXBO8pIpVs0aa9/P2jXwolWPz0adOQ207qc8DspCN7t+KIXi2Zuy6RN+ZtYvl2b7lcTp4jIS6a/447iMN6tiQ6qvxWtdSJiaZv24b0bXvgDj7JGdms2u4lpH5PTKX4PsCVxbZ9GWzbl8GMlb9/u4yOMjo3S6B3oI5UsK5Ux6YJRJXj116ULXvTmTZ/E0u3JLNtn5eI27M/k6Vb9tG9ZX0un7ogZOKpf7uGvKzEk9QO4wPHRGCicy5k1to5txA43czuAW4D3jez4c65jAjEKQLA2ws285e3F4ec8TtuZEfuPa1/uf6clcozqmsz3r5yDJNemMumxMLfnnYkZ3LO07P47/ghHNGrZSVEWHrZuXnc8ObPZPokT8d0a8aFoztHPigRKaTI5JOZDQQGh2g+0swaF3N9A+oBQ4AL8N4Czi9diCJSHW3Zm85Dn67gvZ+3Ftu3RYM6/PnYXpw5tL3vw62ZMbJrM0Z2bQbAEQ/PYN3uVFo1rMuRvVuVe+yhNKwby7DOTRnWuelv55xz7EzJ/G2GVDAxtXrHft+HoHDl5jnW7Eplza5UPlqy7bfz8bHR9GhV/7dZUr1bN6Rn6/q0qF+n3OpJrdiezMOfreLrFTso+LtJckYOJz/+PQ3rxoRcStm3TSDxlKDEk9QKI/Ged6aGSDwV+ofpnLsjMGNqEHAZ8J+KDVHEM/n7ddwbojgzwNVHdOOmY3vVyPqEtVn3lvV596qxXPrifH7etLdQe2pWLpe+OJ97T+vPuJEdIx9gKT35zRoWb95X6HyDOjE8dPagiL2kE5GiFTfz6XS86eAFGXBNKe9leA9jT5dynIhUI/szc3hqxq889926YpMvdWOjuOzQblx+aFfq1ameNSTMjFYN69KqYV0O69nit/O5eY71e1J/mykVrCm1fk8qrhxrrKdn57J4875CD11N68UdsGwvOFuqfin/O3+13FuKkZFd9P/LUImnPm0a8sqlI1WcVmqT4FSBgpV9g//yQ20R9greC7+zUfJJKphzjke/WMXjX/8ass9tJ/Xh0kO6RjAqiaTm9evw2h9Hcf0bP/Ppsu2F2nPzHLe8u4QNiancfFzvKpvAWbJ5H49/vdq37c5T+9GucfWsXyVSE5Xkt5BQ32lK+x1oJ3C7c+6LUo4TkWogJzePN+dv5tEvVrJ7v/8uOfmdOaQ9Nx3Xs9oWtSxOdJTRrUV9urWozwkD2vx2Pj0rl1937i9U5HxnSvmWw0tMzWLW2j2Fdpxr3ySe3vmSUb1bN6Rri3rE+hThnL12D1e+/JNvYdKS6N26Aa9cOpIm9ZR4klol+GxVsMzAfqA+vxcZL2hT4Oi/a4JIOcnLc9z5wTKmzt7g2x5l8MCZAzlnWIcIRyaRFh8XzZPjh3D/J8t59rt1vn3+9+1aNiem88g5g6pc7cmM7FxuePNn352Tj+nbijOHtKuEqEQklOKST+8B6wucm4L39u6/wE/FjM/De9haByxxzpXfnuYiUmXMXLWL+z5azsodKcX2HdW1Kbed1Jf+7apXIcvyEh8XzYD2jRjQ/sCvPyk167dEVP4i56F2HQrX5qR0Niel8+Xynb+di402ujav/9sMqd6tG9CpWQLXvhZ+4qlL83q8culImirxJLXPHqA1XtmB/HbgJZ96hxgX3PWgSQXFJUJWTh43TlvE9EX+S+LjoqN4fNxBHJdvE46arn2T+AOOtU1UlHHrSX3p0DSBuz5YVmh5PcBHS7axPTmDZy8cVqV+rj/y+UpW7yy8IU3TenHcf8YALRcVqWKKTD455xYBi/KfM7MpgT9+FdytRURqp1U7Urjvo+V8u6r4fQS6NK/H307ozTF9W+lhwEeTenGM6tqMUYG6VuAti9iyN/2AGVIrt6ewZtd+snPLb+1edq7zrr8jpcB3/PCN6tKMZvXrlM/FRKqXFXiJpG4Fzi/Cm9V0spld65wrmNk9I3Dcg0gFSM/K5cpXFhywmUV+9eKiefaiYYzp1ty3vaaaesnIyg6hSrhwdGfaNY7nT68uJD278HyBBRuSOOPJH5gyaQRdmhfMrUfenLV7eO57/9la/zh9AM31DCJS5YRTZGVS4FjcrCcRqaF2pWTyry9X8frcjb5vyPJrnBDLdUf1YPzITtqiuZTMjPZNEmjfJIGj+vxeWD07N491u1N/myG1cvt+Vu5I9t21prJ8uGQrt5/Sh4S46lnLS6QMZgFHACMKnH8fOBPoADxrZtc755LNrB7wd+BQvJnl30UyWKkd9qVlc/GL81iwwX/n2SYJsbx48QgGtm8c2cCkSjmqTyvevHw0F784j10+5QDW70njjCd/4JkLhzE83+YrkbY/M4eb3lrkW0PzjIPacXz/2jNzT6Q6KfVvBc65FysiEBGp+jKyc5n8/TqemrGm2OVgsdHGRaM7c82RPbTLWTmLjY6iZyuvZhOD2v52fn9mDqt2pBQqcp6YWnwNrvKWkpHDgg1JHNIjVHkbkRrrU+AW4DAza+icSw6cfxNvE5duwETgAjPbg1egPDgdNA94NLLhSk23MzmDC5+fy4rt/kvj2zSqy9RLRtK9Zf0IRyZV0YD2jXjv6rFMmjKXVTsKL2lLSstm/LNzeOScQZyS7xkkku776BffF25tGtXlzlP7VUJEIlISeiUtUkoTJs9hc1I67ZvE15qp2s45Pli0lX9+upIte4ufXXN8v9b89YTedK4C07Jrk/p1YhjSsQlDOh5YMmZXSmagllQyqwLL91bt2O87rb48+b01FanpnHPfm9mLeLvaDQB+CJzPMrMzgS/xio7H8nudJ4Bc4Brn3NwIhyw12MY9aVwweQ4bE9N827s2r8fUS0dqRzA5QLvG8bx15Riuevknvv91d6H2rNw8rnltIZuS0rjysG4RLafwzYqdvDZ3k2/bP88aSKN4vfAUqarKLflkZg2AhkCx2yA45zaW131FIm1zUjrrdqdWdhgRM399Ivd+tJxFm/YW23dg+0bcdlJfRnSpvKnYUliLBnVo0aAOB/f4vY5HXp5jU1La7zOkArOk1u1OJbe4tZQl5DcdXqQ2cM5NCnF+iZn1Bq4BjgJaAWnAPODJQK1NkXKxYnsyF06eG3I31f7tGvLipBGqzye+GtaNZcqk4dz67hLenL/Zt88/P13JpsQ07jmtv++uueVtb1oWN7+92LftwtGdNNtapIoLO/lkZtHAOGACXl2DBiUc6spyXxGJjI170njg0+V8vGR7sX3bNKrLzcf35tRBbYmKUjHx6iAqyujUrB6dmtU7YFejjOxcftqQxLjn5pT5Hs3qV50dcUSqCudcEnBP4KPcmVn7wLWPB5oB2/B2L747cO+SXGM90ClE8w7n3AEFVczsBeCiYi77tXPuqHxjJuLtoBzKlc65p4sNVnwt2JDEpClzSc7wXyI/qmtTnr1wGA3qapaIhBYbHcWDZw6kY9MEHv58lW+f1+ZuYsveDJ4Yd1CF/326/f1lvsnUzs0S+OsJoTYSFZGqIqwkkJm1wXuQGRY8VV4BiUjl2peezRPf/MoLP6wnK7fgZkwHqhcXzVVHdOeSg7tQN7bYSY9SDdSNjWZM9+b0a9uQZVuTix8QQkJcNEM7acd4kUgys27Aj3h1pN7H23lvBHAdcLyZjXXOlXQ3vX3AYz7nCxeB8Z4J14e4zgSgK/BJiPb3gZ99zs8vKjgJ7dtVu7hi6oKQS6uP7tOK/447SD+3pUTMjD8d2YMOTRP487TFvs+GM1ft4uynZzFl0nDaNKqYJZzTF21l+qKthc5HGTxyzmBtcCJSDZT6X6mZRQEfAEMDp9YBc4Dz8GY1zcDbJrgTMBivpoEDvsB7+yYiVVB2bh6vztnIY1+uIiktu8i+UQbnDu/I9cf0oGWDuhGKUCJpwqhO/PWdJWGPP/2gdnqjLrWSmT0f+ON/nHM/l2Jcf+AGwDnnLgnz9k/iJZ6udc49nu/ajwLXA/cBV5TwWnudc3eVpKNz7j28BNQBzKwx8BcgC3ghxPD3nHOh2qSUpi/ayg1v/kx2rv+65zOHtOfBMwcQE4ElUlKznDa4Ha0b1uWyqQvYl174OXHF9hT+8MQPPD9xOP3aNirXe+9MzuD295f6tl1xWDe97BKpJsL5yXM+XuLJ4b0R6+GcG5ev/d/OuXOccyOB9sC/A30HAP8NVQdBRCqHc44vf9nBcY/N5M4PlhWbeDqkR3M+vu4Q7j9jgBJPNdipg9vStF54y+bM4MLRncs3IJHqYyLeErSOpRzXLjB2Yjg3NbOuwLF4M5CeKNB8J5AKTDCzSO4EMQGIB95xzhWuWizl6pU5G7j29YUhE0+XHNyFh84aqMSThG1k12a8c9UYOjZN8G3fkZzJOU/P4puVO8vtns45bn57MXt9nk97t27AdUf3KLd7iUjFCuenz1mB4xbgL865kOtynHO7nHPXA1cBbYB3zEypaZEqYtnWfYx/bg6XvjSftbuKLqLeo2V9Xpg0nKmXjKR364YRilAqS0JcDA+dNZBwSnjdcHRPerUuaRlAESknRwaOnxd8NnPOpeDtupcAjCrh9eqY2QVmdouZXWdmRwTqfZbGHwPHZ4roM9jM/s/M/mpmEwI1q6QUnHM88c2v3Pru0pAbPdx0bE9uO6mP6jJKmXVrUZ93rhrD4A6NfdtTs3K59MX5vDJnQ7nc7/V5m/hm5a5C52OjjX+dO5g6MVo+KlJdhLM4Njjr6WXnnF8Vw0IJLefcM2Y2DjgEuBL4Rxj3FZFysiM5g4c/W8lbP20udkeyZvXiuP6Ynpw3vIPeltYyR/VpxaPnDOamaYvIKeEOeJcf1pU/Hdm9giMTqZGCz2T+FaKL1ytw9K8KDKvxZkb1BL4qwfVaA1MLnFtnZpOcc98WN9jMRuPNel/lnPumiK7XFfg818yeA/7POZdRgjhrtbw8xz8+Xs5z36/zbTeDe07rz4RRoerHi5Re8/p1eP2yUVz/xs98srTwxjS5eY5b313KxsQ0bj6ud9hJz02Jafz9w198264/pid92uhlqEh1Es5vksG9ugv+lAu+ZQu1DudtvMLkfwjjniJSDtKycnjsy1Uc/tAMpi0oOvEUFxPFlYd3Y8afD+eCUZ2UeKql/nBQO16/bBTDOxc9abVri3r8+7zB/O2EPpjpzbpIGILJo71hjg8WWdkXoj14vnEJrjUFOAovAVUPL4n0P6Az8ImZDSrBNS4LHJ8N0b4OuAbv664HtAXOwVs2eDnwfIhxAJjZZWY238zm79pVeFZEbZCTm8df3l4cMvEUE2X8+7yDlHiSClE3Nponxg3hskO7huzzv2/Xcs1rC8kIUfy+KLl5jhvfXERqVuGxQzo25vJDu5X6miJSucKZ+RT8rSKxwPkUoCHQKsS4HYFj5zDuKSJlkJfnePunzTz8+Up2JBfeoragUwe15S/H96J9E/81/VK7DOvclGlXjGHF9mRen7uJX7Yms3BTEtm5jvp1onlmwjBGd2umpJPUOmbWkNDJnJZmVlzdJ8NLvAwB/ow3s9y/qm7ZBf+BFjuN0Tl3d4FTS4ErzGw/cCNwF3B6yBuZNcJLJIUsNB6YPZV/BlUaMM3MZgOLgPPN7EHn3KIQ458hsJxv2LBhJZuaWYNkZOdyzWsL+eKXHb7tdWOjePqCoRzeq2WEI5PaJCrKuOXEPnRoEs+dHyzDb5L0R0u2sW1fOs9eOIxm9euU+NrPf7+OuesL/roJ8bHRPHLOYKK1hFSk2gkn+bQTr5B4wXmOWwPn+ocYF1zDr/mRIhH045rd3PfRcpZtTS6279BOTbjtpD4c1FGl2aSw3q0bctep/QA44uEZrNudSosGdRnTvXkxI0VqrOuBO3zOG95ModIwvMTQG2HGEpzZFGqbqYYF+oXjabzk06HF9LsAr77U66UtNO6c22RmHwPjA/fxTT7VZikZ2Vz20gJmrd3j296wbgxTJg1naKemEY5MaqsJozvTtnE817y2kDSfmUo/bdzLGU/9yJSJw+naon6x11u1I4WHPl/p23bLib3p0jyS+yaISHkJZx1NcOFtwaIeC/EenE41s/j8Dea9Dp8Q+LTwwmARKXdrdu3n0hfnM+7ZOcUmnjo0jefJ8UN464rRSjyJiJSOFfgIdb64D4DXCb1MrTjB39R6hmgPbgkVqiZUSQS3sCruN79gofHSJuCCguvo9BtmAXv2ZzLu2TkhE08tGtThjctHK/EkEXdUn1a8efloWjbwn920YU8aZzz1I/PyzWZyzrFiezKpmV6pu7SsHLbuTeOGN38mK6fwnlaH9GjOBVpGKlJthTPz6QfgOGBMgfNvA+PwakK9Y2bXA2uBbsC9wCC8N3olKXIpImFKSs3i31+t5uXZG4otEt2gbgzXHNmdi8Z01m4hIiKl9zPwYoFzF+E978wANhYzPg/Yj1f/6Cvn3JIyxBIs6n2smUXl3/HOzBoAY4F0YHYZ7jE6cFwbqoOZjcR75lvlnJsR5n1GFnef2mjL3nQmTJ4Tcnfajk0TePmSkXRspiXzUjn6t2vEu1eP5eIp81i5I6VQ+960bMY/O4d/nNGfzJw8ps7awIrtv/fbkZzJmAf89ydoUDeGf541UEv8RaqxcJJPHwH3AGPMrKVzLvgW7D3gJ7y6BccCy3zGZgD/DOOeIlKMzJxcXvpxA49/vZrkjKI3S4qOMi4Y2ZHrju5J03pxEYpQRKRmcc69D7yf/5yZXRT447+dcx9EMJY1ZvY53jPY1cDj+ZrvxptF9D/nXGogzli8F4TZzrk1wY5m1g/Y5pw7oNiKmXUC/hv49OUiQgkWGn+mqHjN7BDn3HcFzhnwV7wk127g06KuUZv8unM/EybPYds+/w0Ae7duwEsXj6Blw1D7/ohERrvG8Uy7cjRXv/IT360uvOo2KzePm6YtLvV17zmtH20axRffUUSqrFInn5xzC83sLiAe6EBgCrZzzpnZacDnQB+foanAeOec/wJeEQmLc45Plm7ngU9WsDExrdj+R/dpyV9P6EP3lsWvuRcRkVJ7CW/mU3GznirCVcCPwH/M7ChgOd4soiPwltvdmq9vu0D7Bg7cDOZs4K9m9g3ejKwUvCTVSXg7Gn8MPOx380AB9nPxCo0XnBFW0EwzWwXMA7bg1aoai1c7NA3vmbH4YoW1wOLNe5k4ZR6JqVm+7UM6NmbKxBE0SoiNcGQi/hrWjeX5icO57d2lvDF/U5mvd0zfVvxhcLtyiExEKlM4M59wzt0T4vyWwPa75wNH4+18l4b3YPG8c85/S44SMrMHgWF49Qya400f34A36+q/zjn/BfAiNdTPm/by9w9/Yf6GpGL79m3TkNtO6qPi0CIiFcg5N7ES773GzIbhzVA/HjgR2Ab8B7i74GymEL4BegEH4c1AqgfsBb4HpgJTnXOh1nSPD/QvSaHxh4ERwJFAU7wliBuBJ4BHnXNacoe3acgfX5zvu908wGE9W/DUBUNIiAvrkV6kwsRGR/HAmQPo2CyBhz4r29yDuCjTcjuRGqDcf1I553IIPJyU97XxdpX5CfgCb8ZVPWAU3pa/l5nZKOdc2dPrIlXclr3p/PPTFbz/89Zi+7ZsUIebjuvFmUPaa1taEZFKZGZ1gZYAzrkKmRkVeA6aVIJ+6zmwQHrw/LfAt2He+yngqRL2/XM496hNPlu2nWteW+hbeBng5IFtePScwcTFhLN/kEjFMzOuPqI77ZvEc8Mbi8gNmbcu2kdLt/Pn3al01i53ItVadXtN0tA5V2ixu5ndB9wC/A1vyrlIjZSSkc1TM9Yw+ft1ZIZ4GA2Kj43mskO7ctmhXalXp7r9UxcRqZGOA97Fm+Wjb8y12KbEND5eso3d+zMBSEzN4rNl2zmyd0tio6OYNn8TN7+9mFD7howf2ZF7Tuuvl0pSLZwysC33ffQLO1P8l46WxMuzN3DbyX3LMSoRibRq9eDjl3gKeBMv+dQjRLtItZaTm8eb8zfz6Bcr2b2/6B/cZnDmkPbcdGwvWjdS4VERkSpIGYNaau66RP737Rq+XrmT/JNA9qVnc/nUBbRqWIderRoyc/WukNe45sju3HBMTy1Dkmpj3vrEMiWeAN7+aTO3nNiHKCVcRaqtapV8KsIpgWPpt04QqeK+XbWL+z76hVU79hfbd1TXptx2Ul/6t2sUgchERESkJJxzPDNzLfd/sqLIfjuSM9mRHDrxdPvJfbnk4C7lHZ5IhVqzK7XM10hKy2ZfejZNtEuzSLUVdvLJzJoCF+NNIe8LNAHqlGCoc86VKellZjcB9fF2RhkGHIyXeHqgLNcVqUpWbk/hvo+XM3NV6IfQoK7N6/G3E/twdJ+WehMqIiJSxTz33bpiE09FiY4yHjxzIGcNbV+OUYlERnq2f8H80krNylHySaQaCysJZGbHAS/j7U4CkZ8+fhPeTnpBnwITnXPF/5YuUsXtSsnkX1+u4vW5G0PWeghqnBDL/x3Vg/GjOhEbrYKjIiIiVc2iTXv5xyfLwx4fFxPFE+OGcEzfVsV3FqmC6teJLpfrNKgTWy7XEZHKUerkk5n1At4D4vg96bQJ2AJklltkRXDOtQ7E0goYgzfjaaGZneyc+8kn5suAywA6duwYiRArxITJc9iclE77JvFMvWRkZYcj5SwjO5fJ36/jqRlr2J+ZU2Tf2Ghj4pjO/OmIHjRK0A9iERGRquq579cR5iZfAPzj9P5KPEm11qdNwzJfo22jujSMrykVY0Rqp3D+Bd+Mt7zO4SWh/uycW1OeQZWUc24H8K6Z/QSsAl4C+vv0ewZ4BmDYsGFl+PFfuTYnpbNud9nXTEvVkpfnmL54K//8dCVb9qYX2//EAa25+fjedGqm7WZFRESqsp0pGXy6dFuZrrF0SzJnDS2ngEQqwYB2jejdugErtqeEfY1zh3dUaQmRai6cdTpH4iWefnTOnVFZiaf8nHMbgF+AfmbWvLLjESmp+esTOf2pH7nu9Z+LTTwNat+IaVeM5snxQ5V4EhGpnnYDMwMfUgt8tXwn2blle+/50ZKyJa9EKpuZMWF0p7DHx0QZ543oUI4RiUhlCGfmU+vA8ZXyDKQctA0cy6einUgF2rAnlQc/XcHHS7YX27dto7rcfEJvThnYVtvLiohUY865H4DDKzsOiZxt+zLKfI1dKZnk5jmi9Qwg1diZQ9rz0o8bWLmj9LOfLjmkC60a1q2AqEQkksJJPiUBLfHe3kWMmfUG9jrnthc4HwXcG4jpR+dcUiTjEimNfenZ/Pfr1bzw4/pi34TWi4vmqiO6c8nBXagbWz6FGkVERCRycnLzyuc6eXlER+lZQKqvurHRPD9pOGc/9SNbS5GUPXFAa/5yXO8KjExEIiWc5NMS4Cgg0pW7jwceMrOZwBpgD96Od4cBXYHtwB8jHJNIiWTn5vHK7A38+6vVJKVlF9k3yrx17Tcc05MWDepEKMLqpX2T+AOOIiIiVVGThLJvC58QF02dGCWepPpr1zied64ay5WvLGDhxr3F9r94bBduPamPZv2J1BDhJJ8mA0cD5wKPlm84RfoSr2j4WGAQ0BhIxSs0PhX4j3MuMYLxiBTLOceXy3dy/8fLWVuCYvGH9mzBrSf2oVfrBhGIrvrSbo8iUl0EZmh3A5oAJVo34pxTTagaYmTXpmW/RpeyX0OkqmjdqC5vXzGGH9fsYers9Xy5fCe5eb+vBmgUH8s5w9ozbmQnujRXjVORmqTUySfn3Btmdh5wmpnd7Zy7swLi8rvvUuDqSNxLxM++9GzW7tpPepZXViz/D0o/S7fs476PljNr7Z5ir92zVX1uObEPh/dqWS6xiohI5TKzw4Gb8DZqKc00Vkd4LwelChrYvjED2zdi8eZ9YV/jglHhF2oWqYqiooyDezTn4B7NSc7I5oTHvmPL3nTaNYlnxk2HExsdzp5YIlLVhftwcx7wPHCbmY0G/gPMds5FtA6USEVzzjFnXSJTZ2/gs6XbycmXcNqYmMaVLy9gwqhOjO7W7LftX7fvy+Dhz1fy9k+bccVscNO8fhw3HNOLc4a1J0Y/aEVEagQzuxO4I/hpZcYile/C0Z25adqisMZ2aBqvF1NSozWsG0tcjPcMHBcdpcSTSA0WMvlkZiXZNc7w6j8dFRhTkns655ze6EmVty89m+teX8iMlbtC9vlk6XY+Wbqdg7s356GzBvL6vE08M3Mt6dlF//OJi4ni0oO7cOXh3WhQN7a8QxcRkUpiZscB+WeFbwS+BbYAmZUSlFSq0w9qx/s/b+G71aV7RxsTZTxwxkDVuxERkRqhqCRQSX/S6Sei1Dj70rM575nZLN+WXKL+3/+6m4P/+U2xS/EAThvclj8f14v2TRLKGqaIiFQ9fwoc8/CW3f3bueLmwUpNFh1lPDl+CBOnzGPBhpJtyhwTZTxyziDGdm9ewdGJiIhERlHJp5l4dQdEahXnHNe+trDEiaeg4hJPwzo14baT+zK4Q+MyRCciIlXcCLznpzecc49VcixSRTSoG8srl47k/o+X89q8TWTl5IXs26Nlfe45rT+juzWLYIQiIiIVK2TyyTl3eATjEKky5qxL5NtVoZfalVbHpgn89YTenNC/dUmXpoqISPXVKHD8tFKjkCqnbmw0d5/Wn/87uifTFmzig0Vb+WVrMnnOmx114oA2jBvRkVFdm+p5QUREahzVXhIpYOrsDeVynQZ1Y7j2yB5cOKYTdWKiy+WaIiJS5W0HOgBZlR2IVE1N6sVx2aHduOzQbhzx8AzW7U6lY9MEHj//oMoOTUREpMIo+SSSz760bD5dsq3M1zl3eAduPr43TevFlUNUIiJSjfyAtytwv8oORERERKSqUPJJaqX0rFzW70ll/e5U1u1JZd2uVNbvSWX1jv3klkOls9MPaqfEk4hI7fQ4cC5wkZnd75xLr+yARERERCpbscknMxsFvBr49FPn3FWluYGZPQUch1d88xzn3IJSRykShsycXDbuSWPdbi+xtG53Gut272f97jS2J2dU6L1TM3Mq9PoiIlI1Oedmm9kdwN+Bd8zsPOfcvsqOS0RERKQylWTm00NAZ2AzcGsY97gNOBloCzwIHB3GNUR8ZefmsSkx7bfk0vpAomntrlS27kunsja3TojTpEIRkdrIzA4FvgdeA84HVpvZS8BsYDcQepuzAOfczAoNUkRERCTCivwN2cz6A2PxZi3d45xLKu0NnHN7zOwe4H/AEWbW2zm3IqxopVbKzXNsSUpnXXCZXCDBtH53KpuS0snNq6QMUwhm0K1lvcoOQ6RCtW8Sf8BRRH4zA++5icCxOXB9KcY7VBZBREREapjiHm7OCRz3AFPKcJ8pwH1AM7w6CHeX4VpSA+XlObYlZ/yeXPptqVwqGxPTyC6PQkwRckSvlrRsULeywxCpUFMvGVnZIYhUZVbM5yIiIiK1SnHJp9F4b+A+c87lhnsT51yOmX0GjAfGhHsdqd6cc+xKyWTt7t8Lfa/fncr63d6yucycYlciVIiYKKNjswS6NKtH5+b1yMtzTPlxfdjXmzCqU/kFJyIi1Y1esImIiIgUUFzyqXfgOL8c7jUfL/nUpxyuVetk5eSRneslZ7Jz88jJzSMmOqqSoyrMOUdiahbrDlge5xX93rAnldSssHOYZRJl0L5JAl2a16NL83p0bpZA58Cf2zWOP+C/pXOOlTtS+HHNnlLfZ3jnJhzWs0V5hi4iItWIc07JJxEREZECiks+NQkcd5TDvYLXaFoO16o1lm9L5uXZG3hv4ZbfEjebk9I56N4vOHNIey4Y1YnuLetHPK59adms25PKut37Dyj0vW53KikZlbPTmxm0bRRP5+YJgQRTINHUvB4dmiQQF1OyZJ2Z8eT4IZz19Cx+3bm/xPfv0rweT10wlKgora4QEamtzKxj4I+ZzrnyeH4SERERqfaKSz5FFTiWRfAa+s28BFIzc/jL24v5aPE23/aUjBxe+HE9L/y4nrOGtufvf+hP3djoco1hf2bObzWY1hVYKpeUll2u9yqNVg3r0LlZPbq28BJMwRlMHZsmlNt/g8YJcUy7fDRXvrKA2WsTi+0/rFMTnp4wlOb165TL/UVEpNpaj1ey4EngmsoNRURERKRqKC75tAtoD7Qqh3sFr1H6tUy1zP7MHMY/N4dFm/aWqP9bCzazOSmNFyaNKHXyJT0r97ed49YeUOg7jd37M8OIvnw0rx93QGIpOJOpc/MEEuIiswlQk3pxvHrpKL77dTdTZ23g6xU7KLix3pG9W3LBqI4c3rOlZjyJiAhAJhAHzK3sQERERESqiuJ+i9+Ml3waC/yrjPcam++aEoJzjv97fWGJE09Bs9cm8rd3lvCvcwcXasvMyWXjnrTfajD9PpMpje3JGeUTeBgaJ8T+tjQuuDyuS7N6dGqeQMO6sZUWV35RUcZhPVtwWM8W7ErJ5Ned+/m/NxayIzmTDk3jeX7i8MoOUUREqpZtQCegctagi4iIiFRBxSWfvsXb8e4YM2vqnCt+/ZEPM2sKHIs3Df3bcK5RW8xau4cvl+8Ma+y7C7cwoF0jHBywXG7rvnScK3Z4hWhQJ4bOvyWWEujS4vdaTI0T4ionqDC1aFCHFg3qBGZeZRITVfUKvouISKWbg5d86l/ZgYiIiIhUFcUlnz4C/grUB+4B/hTmfe4NXMMFrikhvDx7Q5nG3/PhL+UUScnFx0YHlscl/LZUrmsg4dSsXhxmWo4mIiK1xnPAucBEM7vfOVfynStEREREaqgik0/OuR/M7Ae8JXNXmtka51yplt+Z2Q3AlXiJp1nOue/DjraGS0zN4rNlVXNjnLiYKDo3SzhgB7ngcrmWDeoowSQiIgI4574ysyeBq4APzexc7XonIiIitV1JKjffhLdULhZ42MwOBe50zi0uapCZDQLuAk4NnMoGbgw/1JpvxfZkcgtWtI6g2GijQ9MEugRmLwVrMHVpUY82DeuqoLaIiEgxAs9J04AuwAnAajN7G/gO2AKkF3cN59zMCg1SREREJMKKTT455+aY2ZXAZLzZS6cCp5rZMmAW8CuwN9C9MdANGAP0C5wLZiyuds7NKbfIa6D9GRVfmzQ6ymjfJP73GUzNEgLL5OrTtnFdYqJVx0hERKQMZuA9LxE41gcuDHyUhKNkLwdFREREqo0SPdw456aY2X68BFT9wOl+/J5g8hNMOqUClzrn3gg7yloiPi66XK5jQNvG8YHlcQculevQJIG4GCWYREREKlDBqcKaOiwiIiK1WonfrDnnppnZXOAWvLd3dYoZkgm8BDzgnFsXfoi1R+dm9cp8DQN+/NuRtGkUX/aAREREpLTuruwARERERKqaUk3rds5tAC43s78Ah+IVIm8HNAt02QNsBX4AvnXO7SvHWGu8Dk0TGNGlKXPXJYZ9jaP6tFTiSUREpJI455R8EhERESkgrJoCgaTS9MCHlKMJozqVKfl0wahO5RiNiIiIiIiIiEjZqPhPFXN8/9b0atUgrLGDOzTm0B4tyjkiERERqS7MrL2ZPW9mW80s08zWm9ljZtakFNdYb2YuxMd2n/6di+jvzOz1Iu51kZnNNbP9ZrbPzGaY2cnhfv0iIiJSNWk3lSomNjqK5y4axhlP/ciulMwSj2vXOJ7/TRhKVJRqmoqIiNRGZtYN+BFoCbwPrABGANcBx5vZWOfcnhJebh/wmM/5/UWMWQS853N+aYh4HwZuBDYDzwJxwHnAdDO7xjn33xLGKiIiIlWckk9VUIemCbx9xRgmvTCXNbtSi+3fr21DJl80nFYN60YgOhEREaminsRLPF3rnHs8eNLMHgWuB+4DrijhtfY65+4q5f1/LukYMxuDl3haAwx3ziUFzj8ELAAeNrMPnXPrSxmDiIiIVEFadldFdWyWwEfXHsI/zxrIgHaNfPsM7dSEx84dzHtXj6V1IyWeREREKpuZ5ZbxIyfM+3YFjgXWA08UaL4TSAUmmFnZt9YtH8Ek2H3BxBNAINn0BN6uypMqIS4RERGpAJr5VIXVjY3mnGEdOHtoe1buSGHC5LnsSsmkZYM6vPrHkXRvGV5tKBEREakwlbX+/cjA8XPnXF7+Budcipn9gJecGgV8VYLr1TGzC4COeImrxcBM51xuEWPamtnleLsg7wFmOecWFxPvpz5tnwC3B/rcWYJYRUREpIqrNsknM2sGnA6cBAwA2gFZwBJgCjCl4MNWTWFm9G7dkPp1YtiVkkm9OjFKPImIiFRNMwFXTJ8ooDnQE4gO9F8IpJThvr0Cx1Uh2lfjJZ96UrLkU2tgaoFz68xsknPu2xBjjgl8/MbMZgAXOec25jtXD+85br9zbluIWAnEKiIiIjVAtUk+AWcDTwHbgG+AjUAr4AzgOeAEMzvbOVfcA5+IyP+3d99xdxV1Hsc/X0roEkoogvQqSFuUFQQDoamoKC5YCV2aCLLrShEDiwVFQGRFwEAgiguiICCRFh4ggChNinSIdEILHQLkt3/MXJ/z3Nz23Kfc8nzfr9d5nXvnzJkz97RnnjlzZszMhkREjG00rqTRwAHAkcCCwC4R8XCTmy69o/9yleWl8NENpHUWcD1wD6lCbBXgQGAfYIqkj0bE3wvx3wD+h9TZ+CM5bD1gArAlcLWkDSKi1JHlgPMqaZ+cH1ZYYYUGfpKZmZm1Uif1+fQA8Blg+Yj4SkQcFhF7AGsBjwM7kSqizMzMzNpeRMyMiO+Tyi9rAhdLWnCINld6HbDuQ7qIODoipkbEsxHxRkTcHRH7AicAC5AqlYrxZ0TEURFxW/5NMyPiOlJLq5uB1YC9mshz1bxGxOkRsXFEbDxmzJgmkjYzM7Ph1DGVT7kQdEmFfgyeAX6Zv44d9oyZmZmZDUBETAEuJj1QO6DJZEqthSqPUgLvK4vXjFJ5a4tGIkfEu6TW6eXr1MtrvZZRZmZm1mE6pvKpjnfyvKkRYszMzMxabAqpddIuTa5/f55X6ydp9Tyv1idUI2bkeX9GzHuufJ38+t2TwMKSlq2wzmDk1czMzNpIx1c+SZoH2DV/rTRiipmZmVm7ezHPV21y/WvyfFtJfcp3khYBNgPeBP7SZPoAH83zR2rG6uvfq6wzNc+3r7DOJ8rimJmZWYfr+Mon4EfAusBlEXF5pQiS9pF0i6RbnnvuuUpRzMzMzFpplTyft5mVc0flVwArMeere0eTWh6dU+r0W9K8ktaS1KeyS9I6khYvT1/SisAp+euvy5ZtImlUhXW2Ag6ptA69r/AdIWmxwjql/L9N6vjczMzMukBTo91JeoTUCeRuEXH94GapX/k4CDgUuA/4WrV4EXE6cDrAxhtv7NHwzMzMrG3kUe/2JZWtHh1AUvsDNwInSxoH3AtsQhpx7gHgiELc5fLyf5IqrEr+A/iOpGtyXl4ltcb6FDA/cBlwfNl2jwPWkdQDPJHD1gO2yp+/GxE3FleIiBslnQB8C7hT0gXAKNJrh4sD34iI6f3eA2ZmZtaWmqp8IhVSgjTiSR/5adm6AHmkkyEh6QDgZ8A/gHER8WKdVczMzMwGjaTSa/9TI+KJmpHnXHce4P2kCprD6C1bXdRsfiLiYUkbA8eQXmf7JPA0cDJwdINlpWtII+9tSHrNbiFgJjANmAxMjojyB3mTgc8BHya9Mjcv8CxwPnBKtQeVEXGopDuBA4F9gNnAbcBPIuLSBn+2mZmZdYBmK59q2Ry4kFSAGIr0kXQwcCJwN6niaUbtNczMzMwG3SRShdHnyC1+JL03gPT+yZytivolIh4Hdm8g3nRSB+fl4dcC1/ZzmxOBif1Zp7Du2cDZzaxrZmZmnaNm5ZCkq0idPfYAN0dEfwpUcxRoBoOk/yb183QHsE1EPD8U2zGrZvnFFugzNzMzK2i2/HM9MD4iXh7MzJiZmZm1g3otk7Yi9RMA8Iak60kVUS0h6bukpuS3Atv6VTtrhcl7btLqLJiZWfu6jtQaqp63Sa+z/QP4c0T8dSgzZWZmZtZK9SqfJgFjSf0QLETqP2C7wvKjJW0K3AT8ZSif1kkaT6p4eo/0dPAgaY6Hi9MjYtJQ5cHMzMyslogY2+o8mJmZmbWbmpVPEbEH/GvY23GkllBjgWVzlE2Aj5SiS7ofGKrX4FbO87mBg6vEuZZUYWZmZmZmZmZmZm2goQ7Bc6eU/+pMUtLsvGgaaaSWVUh9HKxNoam5pIdIo6ZcA1wTEU83m9GImABMaHZ9MzMzMzMzMzMbfgMdje7YiLhC0hjScLwfBbYlDc8LqVJqZaDUguoB0nDEBwxwu2ZmZmZmZmZm1gHqjXa3eCOdekfEc8DFwMWS/gJcmBcdQ+qw/CPAfMCawBqAK5/MzMysWxwkacdBSisiYs9BSsvMzMysLdRr+TRD0p2kEe6mAtdFxCuNJl56VU7S/MCmpIqosU3l1MzMzKw9bVk/Sr+48snMzMy6Sr3Kp7mADYD1gW8CsyXdXlg+XyMbiYi3SJVXU5vIo5mZmVk7m2P43QGI+lHMzLrH8ost0GduZt2pXuXT50ktlcYCHyKNNLcxqWAk4KI8wt2NwA15PpgFMDMzM7N293PgtlZnwsysE03ec5NWZ8HMhkHNyqeIuAi4CEDSYsDHSRVRB9FbAbUWqS+n3fNqs0rrS1o3Iu4e5DybmZmZtZOrI+LiVmfCzMzMrF01PNpdRLxEqoi6SNJBOfi4nMamwEak1/Dmo7di6u+SXgCuJfUbdU1E/GOwMj/SuEmqmZmZWfdw2c7MzEaKhiufqrgmIq4AkDSK9EreeGBveiuglgR2Ir3Ch6TnImKZAW53RHKTVDMzM7Pu4bKdmZmNFHMNVkIRMSsibgQuKwSvR+qo/I/ATFJl1JjB2qaZmZmZmZmZmbW3gbZ8qin393Q38HNJIo2cN9jDEZuZmZmZmZmZWZsaSOVTv0a1i4gAbs+TmZmZmZmZmZmNAE1VPkXEoL2uZ2ZmZtbB+vUwzszMzGwkGorX7h4Dzh6CdM3MzMzaycp5PqOluTAzMzNrc4Ne+RQRtwO7D3a6ZmZmZu0kIv7Z6jyYmZmZdQK/PmdmZmZmZmZmZkPGlU9mZmZmZmZmZjZkXPlkZmZmZmZmZmZDxpVPZmZmZmZmZmY2ZBQRrc7DsJL0HNDJHYQuCTzf6kyYj0Mb8DFoPR+D1uv0Y7BiRIxpdSass7lsZ4PAx6D1fAxaz8eg9brhGFQt2424yqdOJ+mWiNi41fkY6XwcWs/HoPV8DFrPx8Cs8/k6bj0fg9bzMWg9H4PW6/Zj4NfuzMzMzMzMzMxsyLjyyczMzMzMzMzMhowrnzrP6a3OgAE+Du3Ax6D1fAxaz8fArPP5Om49H4PW8zFoPR+D1uvqY+A+n8zMzMzMzMzMbMi45ZOZmZmZmZmZmQ0ZVz51MUkhqafV+TDrD0lj87k7odV5sWSw7iWSeiS5ue0Q6+/xGsxrzn93zIaWrzHrNC7XtR+X6zpPt5TtXPmUSTo379j9Goh7ZY674zBkzQZRPm6+SRaU9omk2ZJWrRHvmkLc3YYxi21D0qT8+1dqdV76q5FzX9L0Tv19zfK936x7+foeGVy268vlusa5XNedfO9vX6586lXq3GvvWpHyxTsOeBq4dIjzZDZc3gUE7FlpoaTVgY/neDbyrA3s2upMDBHf++GvpGN8SqszYjbIfH3bSOVyndXSzeU68L0f2rRs58qnLCJ6gAeADSVtVCPqnqSb+VkR4Ru2dYtngVuA3SXNU2H5XqTzvttuzNaAiLgvIh5rdT6Ggu/9EBFv5GP8fKvzYjaYfH3bCOZynVXVzeU68L0f2rds58qnvs7I84q1pJLmBnYHAvhVIXyt3GzzcUlvS3o2N/dbs0Iapeadq0j6hqQ7Jb2Z35ndPi87s8r255P0fJ7ma/ZHSlpU0g8l3S/pLUkvSbpc0tZl8dbM+flNWfjKhWa6m5ct+3EO36rZ/LWLvL+/k4/RG5JekXS9pJ3L4i0saZakG8rCF8j7NyR9rWzZ/jl8j+H4LQ06A1gG2KEYKGleYDxwI3BPtZUlrS7pHElP5v3xVP6+epX4S0uamK+XNyXdIWl8rQxKWjyfu/fmdV6WdLWkbSvE3a3UlDxfWz05fhTi7Cjp15IekPS6pNck3SrpIElzlaUXeT8APFq4BqY3m8dOoSrvektaVtJZkmYUj6HqvGcuaR5Jh0t6MN8zH5d0nKRRQ/1bqujKe7+kJSWdLunpnL97JO1eIV7V4yXpw5KukPRqvgdeJemjkibkdcYOZNtmw6Arr+8K6bhs1wCNrLKdy3Uu11Wk7i/XQZfe+9XpZbuI8JQnYAzwNvAysGCF5TuQTtArCmHbA28A7wB/AH4MnAu8ldPZqCyNSTmNS4CZwG+AHwHfJ9W8PgS8DixaYftfzuse3+DvCaCnLGw06Q9NkJrj/Yh0wb0CzAa+Xhb/CeDpsrC98voBTChbdgvwJjB/q49njX0SDcQbBfTk+PcCPwH+l/QkKYAflMWfls+BRQphWxf206Sy+Bfk8BXbZJ88ASwCvAZcWrZ8pxxnN+DY0ueyOB/O5/ts4CLgB/l6eC+Hb1wWfwng4ZzW9cAP87XxJvDHKufWisCjedl1wImkZrVP5e3uXRZ/txz3UlKz8kuA44DzCnHuA/4BTM7XwqnA/Xm9yWXpTQDuyMtOyt8nAAc3m8d2O/eB6TneShXW7SkLW6rwW6/Nx/As0v3rwirHsCeHn09q4nxm3pcP5PCzWrRvuvHef0c+l+8Cfk4qhL2Ul40viz+2yvHanHRNvgucR7quL8m/8bK8ztiBbNuTp6GeuvT67ikLG43LdtFAvBFRtsPlOpfreuNMZwSW63LeuvHefwcdXrZrycnQzlM+CHPchPOy0s3zC/n7YnmnPw98sCzuOqQb/m1VTtIngZUrbOM/8/IDKywrXeBr9OMk7SkLOy2HnwaoEL56vqjepnCDAs7J8dcphP0WeA64Hbi+EL4Y6Y/S1a0+jnX2STQQ77Ac9zJgnkL4UvTeyDcthB+Twz5VCPthvrCnAo8XwufK58zDrd4fhX3yRP78q5zn5QvL/5zPjQWpUEgh3VzvzeFfKUt7lxx+HzBXIfz0HH5iWfyNSTf8an/gZgNfLAsfTbohvgksXQjfLaczG9i+ym9ftULYXMDZed1NypaVrt+VqqTXrzy24tynt3BVaZpZ6fdR+V4yMYcfVxa+Puk+UquQciuweCF8IdIf6PeAZVq0f7rt3h+k63nuQvgHSdf3P8rijy0/Xvk6eDCHf6Is/r6FbYwdyLY9eRqOqQuv756yMJftXLYr3x8u1/U9Ni7XzbluT1lYV5Xrcj667d7f8WW7lpwI7TyROh0LYFpZ+LKkm+czwLw57Js57gFV0joxL/9gIax0kn6zyjpLkG5kd5WFr5nXm9qP39LnxgLMS6p9fbV4gygs/5+8zlGFsPE57KBC2DP5Yv4JMAtYOId/Psc9vNXHsc4+iQbiPUj6Y7NWhWV75nTOLIR9PIedUAj7K3AzcEDx5gJslL+f3ur9UdgnpULKJsVzgPTE5z3gF/l7pULKZjnsxirpX5+Xb1F2Hr5C5ScBpWtkQiFs/Rz2uyrb+Gxevn8hbLccdmET+6R0jI6qkreVKqzT7zy24Dg3Oq1UYd2ewvdRpCdDMyk8ES4sP6P8GObwnhy+dYV1js7LdmjR/um2e//rwPsqLLs2Ly8+yR9b4Zr7WLXtkgovpSfJYweybU+ehmPqwuu7p/DdZTuX7SrtD5fr+qbnct2c6/YUvndduS7nodvu/R1ftqvUAd1IN5XUbHQzSWtHxL05fHdgHlIT23dy2EfzfP0q78Cukedrk5qAFv210sYj4gVJ5wO7Sto0Im7Mi/bJ81/269f0tRbpKccNEfFiheVTgSOBDQthV+f5OOBkSesCS+fwx0k1upsDU4CtCul0LEmLAKsBT0bEfRWilH5fcT/dRLq5jMtpLEr6Q/fjQvxxpGaobbufIuJmSXcBe0g6ltQMfy5635uupNSRX7XfM5V0s9uQ1GS5dB5eHxEvV4jfQ+87+CWla23RKtfamDxfu8KyitcagKQlgP8CPgmsQnpSU7RctXUrGEgeh01EqNqy3M/Big0ksyawAHBLRLxaYfk00rlTzS0Vwh7P88Ua2P5Q6LZ7/4MR8UqF8NJ+Hk36Z7Wa0v1tWoW8zpZ0I72/c7C3bTbYuu36LnLZrgEjtWzncp3LdYzcch10372/48t2rnwqExEh6VekZrV7AYdKErAHvc3NSpbI85rDOAILVwh7pkb8X5CGv/w6cGPuhGw8MIP03nWzFs3zp6ssL4WPLgVExBOSHgTG5o7ZxuVFV5N+wzs5bEqev0LlG1AnaWY/zZI0Ddha0lKkG9jcpGbq90p6irR/TqW3Fr6tCigFZwAnk9573h24NSJurxG/v/urFP/ZKvErXRula22bPFXT8LUmaTTwN2Bl0h+Nc4AXSc1HR5OegPSn89eB5LHT1DuG1cIBiIiZFYJLo4zM3WSeBqQL7/0zq4Q3up8HcowHum2zQdWF13eRy3aNGcllO5frXK6rp+vKddCV9/6ZVcI7pmzn0e4qO4v0h3fX3Ev/VsCqwDUR8VAhXql2f/2IUI3p7ArbiGobj4ibgduAnSUtRuoYcAlSp22zBvC7SvldpsryZcvilUwF3kfqfHAc8FhEPBwRr5Nu7ltLWpb05OO66PyhKgeyn0Q6X8aR3o8ujZJyDbBlvuFsDtwTETMGLceDazLpSd9ppCdEp9eJ39/9VZovXSV+pXRK63yzzrVWacSFatfaXqQCytERsUlE7B8RR0bEBNKrB/01kDx2mtKTj2rHsFp4u+vWe38zuvUY28jVrde3y3aNGcllO5frXK6rp5v/5nfrvb8ZLT/OrnyqICKeBS4GlgR2pLeZYfnN+i95vjmD71RgflJN6T6kk7pWE9lG3E96n3eDfPKX2zLPbysLLzXP3g7YAriqbNl6wBfL4nasSM1NHwaWU+XhZOvtp3GkG9sNEfFWYdniwH6kJsBtu5/y04sLgOVJ7/f+ts4qpadnY6ssL4WX9td99J6Hi9aIXzQU19pqef77Css+XmWd9/K8Uu3+UN4P2s19pILsevlVhnIfG+b8DIouvvc3o3Rdz3EslYar3nR4s2M2MF18fbts14CRXLZzuQ5wua6erizXQVff+5vR8rKdK5+qK50QhwKfI/V8f2FZnLNITdC+J+kj5QlImkvS2Ca3fy6pBvbbpBvmlRHxcJNpAan5MGkIyIVJI3j8i6RVgYNINcOTy1a9htyhHqm5XvGPa+mJ0HcK37vBmaTf9ZPcJB0ASUsC3y3EKbqVdD58ljQqQnE/lT4fluftvp+OJJ3320Xld7+LbiAVfj8m6QvFBfn7FqT+EKYBRHq3+jekIYAnlMXfGPhK+QYi4hZSB5efl7RHpUxI+lBuFt+o6Xk+tiydDek9TuVeyPMVhimPbSnfS84j3Q+OLC6TtD7pj2un6rp7f5NuIP2jtqWkT5Qt24fqfQKYtbOuu75dtuuXkVy2c7muMpfr6PpyHXThvb9JLS/buc+n6q4AHgVKJ98p5U3jInUi9gXSyfsXSVcD95BG0liB9G74EqSazn6JiDcknU0qNEBqKjsYvkOq0T1Q0odJhY8lgZ1JfzQOjIhHy/LyvKQ7SaM+QN8/rjeRnnYsRRqi965ByueQkjSpxuL9geOBT5AKG3+XdBmpM8X/IP3WH0dEn87aInXUdm1eBwoFlIh4TNLDpGae75FGBmhbEfEY8FiDcUPSeOBK4DxJfyQ9QVmT9IThVWDXiJhdWO1w0lPEg3PBZBqpGfcupCGQP1NhU18mnXsTJR1EGm1mJulJ3nrAuqRrrtEm7+eQOqU8SdKWpFFwVgd2AP6Q81Lu6rzOGZIuIA27OjMiThmiPLaz75CeAn9b0ibAjaRjuDPpGO5Iuhd2mm699/c3H7Ml7UUakvtiSb8nFVjWI/V9MYV0j+zEY2wjV7de3y7b4bJdLS7XuVzXgG4t10H33vv7m4/Wl+2iRUMfdsIEHEHvEJVr1oi3EnAK6Sb3Ful9yvtIT5l2LIs7iSpDelZId/0c9ylgnibyHxSG0SyEjwaOy/l9m3QTvRLYtkZaP83p3VNh2eV52XmtPmYN7pN60+gcd37SH9O7SU1RXyX9Mf1SjfS/kdN4GZi7bNlpednNrd4PFfbJEw3GnWNI3sKyNfM5/zTpKevTwK+rXTukPgDOJBVs3wTuIA2jO5YKw7nmdRbJx+RWUgHhTdIfkz+RauwXKsTdrVpeC3E+SGqKO4PUFP1WUnPclfK6kyqs8y3g3nztBDC92Ty24tyvE2c6DQzJWwhfDji77BiOB76Q1zm4LH5PtTw0cryGcV915b2/Wj7qXHObkP4+vJqnq0gFsFPyOhsMZNuePA331K3XNy7buWzXd3+4XOdyHbhcV8xPV977q+WjznXXsrKd8krWhiTtRmoCeGxEfLdOdDOztiDp+6SC2vYRcXmr89NpOuHeL+kGUuFl0UgdFJtZAzrh+jYzK3K5buA64d4/HGU7Vz61KUnzkDryWxtYOSKeaHGWzMz6kPT+iHiqLOxDpKbas4DlordjVmtAO937JS0IjIqyIZQLBagpEfHJFmTNrCO10/VtZlbO5bqh0U73/laX7dznU5uR9DFSR2RjgQ+R3kl14cTM2tEtkh4ivb7wOqlvhU+RBrPY1wWUxrXpvX8F4HZJVwIPkcoMG5JGSZlJ6rjTzOpo0+vbzKycy3WDqE3v/S0t27nyqf1sDXwPeJHUM/+3W5sdM7OqTiN1QPklUp8IM0n9hBwfET0ty1Vnasd7/7OkEYw+ThqGfD7gGdKTse9Ha0ZqMetE7Xh9m5mVc7lucLXjvb+lZTu/dmdmZmZmZmZmZkNmrlZnwMzMzMzMzMzMupcrn8zMzMzMzMzMbMi48snMzMzMzMzMzIaMK5/M2pCkyFNPq/NizZM0tnAsJ7Q6P43wuWdmZmZmZoPNo91ZV5G0EvDoICW3e0RMGqS0OoakBYGdSCM0bAyMAUaThlydAdwG9ADnR8RLrcmltYqkHYEN8teTImJmyzJjZmZmZmYdwZVPZgaAJAEHAYcDS1WIMjpPawBfBE6WdBpwdES8MEzZtNbbERifP08iDcNrZmZmZmZWlSufrNvMAD5XY/lWwDfy52uAk2vEvW2wMtXuJC0E/JpUsVDyCDAFuAd4AVgYWJ60DzcDRpH25YvAhOHLrQ2liFCr82BmZmZmZt3FlU/WVSLiDeCiassljS58fSwiqsYdKXKLp/8DdshBL5EqlX4bEbMrrHKMpOWBI4G9hieXZmZmZmZm1qlc+WRmh9Jb8fQ8sEVE3FtrhYh4AthX0vnAmkOcPzMzMzMzM+tgHu3OrEDSPJK2k/RTSdMkzZA0S9Krkh6QNEnSFg2m9X5Jx0i6SdKLkt6R9JKkByVdK+l7kj48gLwuKOmywuhk50ka1c80FgIOKwR9vV7FU1FETI2IU2uk/wFJP5J0W94Hb0t6UtIlknaTNHed/PWUfl/+PpekPXL4DEmvS7pL0hGSFilbdxlJ/yPpTkmvSHpZ0nWSdqmzzQmFfTo2h31S0h8lPZF/wxOSfivpo43uq0ZI2j6fYw/mc+4NSQ/nsI9VWWf+vA9Ked6pzjYuKcQ9osLyiqPd5TwEvf09ATxaiF+aJuX4xxXCar0KW9zGnTn+m5IWa2QdMzMzMzNrf275ZNbXlcDYCuHzAqvnabyks4F9ImJWpUQkfYr0KtvCZYtG52k1YAvgkPy9XyQtDvwJ+Pcc9AvgG1Vek6tlV2Dx/PnOiPhDf/NSjaSvAycCC5Qten+edgC+JekzETG9gfQWJr1SOa5s0brAscBOksZFxEu5UuiPpJH6ijYHNpf04Yj4zwZ/x/8C+5cFL0fqdH1nScdExNGNpFVjG2NI58tWFRavkqfxkiYC+0XEO6WFEfGWpC8DfwXmB86QdHNunVa+nQPobeV2HfDDgeS7jtOA/wIE7A1cWCuypE2AD+WvF3gkRTMzMzOz7uHKJ7O+FgBeA64GbgWmA28BywLrAF8BFiK1/pgJHFyegKTl6Fvx9CdSpdZTpNaGSwHrA9sAi/Y3g7m/pcuBD+agoyNiQn/TybYpfJ7cZBpzyBVPvywEXULaDzNJo+XtDqxMqmyYJmnDiHiuTrJnkSqebgDOB54BVgQOyPMNgZMkfY+0f0YBvwKmAbNIFU97k+57h0q6PCKurLPNb5I6YX8+p3UnsCCwPbAT6XhOkPRCRJxSJ62KckXiTcCqOegfwO+AB4DZpPNuN1Jn73vm/O9WTCMi7pL0bVIH+osBk3NF3L8qIyWtAxyfv74EfLWflZUnkyr/DgK2zGFfJ3XyX/RYztMjkq4EtgW2k7RCRDxWI/29C5/P6Ee+zMzMzMys3UWEJ08jZiL90x55mlRh+ThggRrrLwFcn9d/D1i5Qpz/LGzj2zXSErB5lWWl9XvKwtcm/XNf2v5+A9wfzxa2tdkg7eOVgDdymu8CO1eIswBwaWHbv6uSVk8hTgCHV4gzBniysL07SBUi61WI+7VCWpdV2eaEsm3eDSxVId6OwDs5zuvAChXijC2kM6HK9i4sxDkSmKtCnIVJFWqleNtXSau4Tw8rhM9HqjgrLftCjeNX8dwrLJ9UiLNSnXPh8/V+f+H3vZrj3T8Y56EnT548efLkyZMnT57aZ3KfT2YFEXF1RLxZY/kL9PZ5MxepJVS51Qqfq7bgiOT6RvOWX0u6HvgAqSXPF6NGf0sNpDcvqRVWyUPNplXmIHpftftpRJxfHiHv4y8DT+egnSStUSfdyyPiBxXSeg4otTqam9Sq7MCIuLNC3MnAg/nrOEn1Wn++C+wSEeWte4g0UuJP89cFgf3qpDUHSRuRKrEAzoyIY6NCa6SIeI30mt/LOehbVZLcndQiDODoQp9iP6H3lbaJEXFBf/PapItJLf4AdpdU7W/Ol+htKehWT2ZmZmZmXcaVT2b9FBGP0PsP/iYVorxR+LzOYGxT0vbAVFLLq9eAT0XE7waY7OJl32cOML2Sz+f5u/RWzswhIl4h9VUFqRXYjnXSrfVa2w2Fz88CtSpXpuX5KHpfdavm8oi4p8byk0gt0AAa6lS7zNcKn4+vGguI1AfSZfnrFpLmqxDnOVLlaJD6KTtX0s7AN3KUB0ivEg6LiHgXmJi/rgBsVyVq6ZW7WcDZQ50vMzMzMzMbXq58Misj6X2S9sujgk2X9Fr5iF7AMjn68hWSKPYj9AdJh+R+mprNz1dJLUgWBJ4DtoyIq5pNbyhJWorU/xLA3yu1GCpzReFzpYq8optrLHu28PnWSq2HqsStN6La1bUWRsQzQGl0wDUk9bcPr83zfBawpqQda02k1+fI81Wq5OkKUkfvkFrhnVfYxpci4vV+5nGgzqC3gm7v8oWS1gNKLbQuivp9f5mZmZmZWYdxh+NmBZK2BM6lt3KpnveVB0TEFEnnkl4rGwOcAJwg6UHgRtIoY5c2UDEDsBFwDqll0GPANhHxQIN5q+fFsu+j6Vsx04xlC58byWcxzrJVYyUv1Fj2doPxyuPOXyduI68iPkQacU+k8+bl2tH7WCnPR1FnNLgKalWcHUbqFHzDQtgREXFbP7cxYBHxuKQppFH2Pi1p6YgonmfuaNzMzMzMrMu55ZNZJml10ohspYqn+0mvVR1A6pPmc4Wp1Dpj7irJfRXYCyi+srU66ZWoicBTks6VVK/CZW5SpQakipJBqzCOiHfo/R1Q/xW0RixS+NxIC5vXqqw7hzqtmYr6M4JbPW/Uj9Lndy5cNVZl/R7tsGBUtQURMQt4tBD0HvCHAWxroE7L8z4j9Uman3StQMpvzZZmZmZmZmbWmVz5ZNbrMHo7yv4+sHZEHBIRv4iI/4uIi0oTvRVCFeXOxCdGxLqkSp3xpH/AS51dz02q0LpZ0tI1kvobafQ8SJ2DXyPpg838uCqKfSVtOgjpvVr4vFAD8YuVNa9WjdU6CzYQp/g7X6saq7JS/OkRoX5OPdUSlbQXvX1vQTrfJkuqVlk61C4jtdwD2EtS6fr5D1KLO4BfRUQMd8bMzMzMzGzoufLJrNfWeT4DOKraP8KSFmHOzrqriohHIuKciNg3ItYA/g24PS/+APBfddb/KXBo/lqqgBqUjszp2+fSV6vGatzThc+rNxC/GOepqrFaZ7X6Uf4VJ+jtiL5RT+b5ByTN8QpnM/KogSflry/T25poU+C7g7GN/sqt1kqv1K0GfDx/3ivP3wXOGu58mZmZmZnZ8HDlk1mvUgukR+u84rU1A7h2cr87xVHOPtbAOicA38pflwKmDlIF1GR6+35aX1IzI7b9S+7H6p/56waSxtRZZdvC578OZNtDZKtaCyUtA6ydvz4QEf3p7wng2jyfG/h0P9etlJ95SX2WlVpj7QfsQm/F3pGSNhvgZorXRs0WgGUmkiqZAPbOlWRb5O+XRsTTlVczMzMzM7NO58ons16l/n1WKbwW1Ed+benwQdjW9MLnhvpxiogTgUPy10GpgIqI14AfFYJOl7RWo+tL2lLSfmXBv8/zeYCDa6y7CLB/KSv0v8Pt4bC9pLVrLD+I3n6/mulT6ZzC56MkNfKqYi3fJ7WsA5gcEb+NiBeAXUn7eG7gN02MyldUfLWw4fzmyqWL89edgP8uLHZH42ZmZmZmXcyVT2a9/pbnY6hQaZJblZwBbFwrEUlHSdpGUq3ra//C5783msGIOIm+FVCD8Qre8cCU/HlJ4AZJX65WAQcgaXlJpwJX0ttirOTnwJv587cl7VRh/fmBXwPvz0G/j4gHy+O1gXmA8yq14JL0aXr743oDOLW/iUfEzfRW1q0BXFKrDzBJ80jaUdL+FZaNK+TnUeDAwnauJh1ngBWbyWtBsSPzjfq57i/zfD5gj/z5ceDPA8iPmZmZmZm1uUEbOcusC/wc2CZ/PkHSWOBy4AVS30S75vk1eb58lXS2Ao4GnpF0OXAHqS+guUiVLZ8BNs9x3wZO6E8mI+IkSUHq12cMqQJqq4i4uz/pFNILSbuQXtfagdSf1W+AYyRNIY3Y9wKpc/Dl8+/bDJi3SnrTJR1CqmiYB7hA0h9JnU7PJO27PYBV8ipPkkYUbEcXATsC90g6A7iL1An5dqTOsksVdP8dEY83uY09SBVPHwK2BB6RdAFwE/A8aZTDZUkVPduSjs/EYgKSliC1ohLp1bavRMQrZds5gnTs/g34kqQpETG5ifwWR6T7ca6Yu5/eV+qejIi7qqx7FfAwfUdWnNiPkQzNzMzMzKwDufLJLIuISyT9kDTqHaRKos+URbuB1IfO36iu9I/0MqRR7sZXifc8qZLgniby+rNcAfUzUgXU1AFWQL0q6bOkFl/fyWmuSqH1TAVvklrQ/KxCeqflllMnkipPPpuncncDn859RbWjn9FbOVbpdcsAjomIU5rdQES8kvthOoN0bi1IqujctcZq5Z2zT6S3FdkxEXFThe28I+nLwG2k1+X+V9INEfFIP/N7p6TfkkZrXJreFlUlZwO7VVk3JJ0OHJeDZgNn9mf7ZmZmZmbWefzanVlBRBwOfAL4E6ly6B3SCG5Tgb2BsRHxXJ1kdgC2B34CTCO1enoHmJU/X016PWr1iLiiWiIN5PVkUp9D0FsBte4A0pudOzZfmVRh9mvgXlKrp3dJI6c9APwfaV8sGxGHRsRLVdL7JalFz3Gk1l8zSfvgaVIrqN2BDSJierN5Hg4RcSDwKeASUqXPrDw/D9gsIiYMwjZejYgvklo3nUQaDbG0318DHiS1wvoWsGpEHFVaV9K+9Fbs3QD8oMZ2HqD3ldJFSP0/NfMQ4mukzsx7SNfJuzVj93VV4fOfB9BizMzMzMzMOoSqjCZvZjYiSZoAfC9/3TIielqXm+4j6VjSK4AAn4uIi1qYHTMzMzMzGwZu+WRmZsNC0ih6Oxp/Eri0hdkxMzMzM7Nh4sonMzMbLvuSOk8HODUi+vO6npmZmZmZdSh3OG5mZkNC0uLAR4D5gH8HDsmLXiCNLmlmZmZmZiOAK5/MzGyorAdMKQsL4OsR8UoL8mNmZmZmZi3g1+7MzGw4PE8a6XHLiPh9qzNjZmZmZmbDx6PdmZmZmZmZmZnZkHHLJzMzMzMzMzMzGzKufDIzMzMzMzMzsyHjyiczMzMzMzMzMxsyrnwyMzMzMzMzM7Mh48onMzMzMzMzMzMbMq58MjMzMzMzMzOzIfP/Gix6S+6N2p8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, sharey=False, figsize=(20,5))\n",
    "\n",
    "\n",
    "sns.pointplot(x=\"complexity_cat\", y=\"n_chats_round\",\n",
    "              ci=95, data=df,n_boot=1000,\n",
    "              scale=2, ax=axes[0])\n",
    "\n",
    "sns.pointplot(x=\"complexity_cat\", y=\"turn_taking_index\",\n",
    "              ci=95, data=df,n_boot=1000,\n",
    "              scale=2, ax=axes[1])\n",
    "\n",
    "axes[0].set_ylabel(\"# Chat messages\", fontsize=label_size)\n",
    "axes[0].tick_params(axis='both', labelsize=20)\n",
    "\n",
    "\n",
    "axes[1].set_ylabel(\"\")\n",
    "axes[1].tick_params(axis='both', labelsize=20)\n",
    "axes[1].set_ylabel(\"Turn-taking index\", fontsize=label_size)\n",
    "\n",
    "\n",
    "axes[0].set_xlabel(\"Task Complexity\", fontsize=label_size)\n",
    "axes[1].set_xlabel(\"\", fontsize=label_size)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.3, hspace=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true"
   },
   "source": [
    "# Figure S7: Group performance across task complexity, by block "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1296 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(12, 18))\n",
    "\n",
    "sns.pointplot(x=\"complexity_cat\", y=\"normalized_score\", data=df, hue=\"block_cat\", \n",
    "              hue_order=[\"ll\", \"ml\", \"hl\", \"lh\", \"mh\", \"hh\"], \n",
    "              markers=[\"s\"]*3+[\"o\"]*3, \n",
    "              palette=dict(zip([\"ll\", \"ml\", \"hl\", \"lh\", \"mh\", \"hh\"], [\"#FF0000\", \"blue\", \"#1fad1f\"]*2)),\n",
    "              scale=1.5, dodge=0.5, linestyles=[\"\"]*6, ax=axes[0])\n",
    "\n",
    "sns.pointplot(x=\"complexity_cat\", y=\"round_duration\", data=df, hue=\"block_cat\", \n",
    "              hue_order=[\"ll\", \"ml\", \"hl\", \"lh\", \"mh\", \"hh\"], \n",
    "              markers=[\"s\"]*3+[\"o\"]*3, \n",
    "              palette=dict(zip([\"ll\", \"ml\", \"hl\", \"lh\", \"mh\", \"hh\"], [\"#FF0000\", \"blue\", \"#1fad1f\"]*2)),\n",
    "              scale=1.5, dodge=0.5, linestyles=[\"\"]*6, ax=axes[1])\n",
    "\n",
    "sns.pointplot(x=\"complexity_cat\", y=\"efficiency\", data=df, hue=\"block_cat\", \n",
    "              hue_order=[\"ll\", \"ml\", \"hl\", \"lh\", \"mh\", \"hh\"], \n",
    "              markers=[\"s\"]*3+[\"o\"]*3, \n",
    "              palette=dict(zip([\"ll\", \"ml\", \"hl\", \"lh\", \"mh\", \"hh\"], [\"#FF0000\", \"blue\", \"#1fad1f\"]*2)),\n",
    "              scale=1.5, dodge=0.5, linestyles=[\"\"]*6, ax=axes[2])\n",
    "\n",
    "axes[0].set_xlabel(\"\")\n",
    "axes[1].set_xlabel(\"\")\n",
    "axes[2].set_xlabel(\"Task Complexity\", fontsize=label_size)\n",
    "\n",
    "axes[0].set_ylabel(\"Normalized score\", fontsize=label_size)\n",
    "axes[1].set_ylabel(\"Duration\", fontsize=label_size)\n",
    "axes[2].set_ylabel(\"Efficiency\", fontsize=label_size)\n",
    "\n",
    "axes[0].tick_params(labelsize=tick_size)\n",
    "axes[1].tick_params(labelsize=tick_size)\n",
    "axes[2].tick_params(labelsize=tick_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true"
   },
   "source": [
    "# Figure S8: Round index vs collaborative behavior \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, sharey=False, figsize=(20,5))\n",
    "\n",
    "\n",
    "sns.pointplot(x=\"round_index\", y=\"n_chats_round\",\n",
    "              ci=95, data=df,n_boot=1000,\n",
    "              scale=2, ax=axes[0])\n",
    "\n",
    "sns.pointplot(x=\"round_index\", y=\"turn_taking_index\",\n",
    "              ci=95, data=df,n_boot=1000,\n",
    "              scale=2, ax=axes[1])\n",
    "\n",
    "axes[0].set_ylabel(\"# Chat messages\", fontsize=label_size)\n",
    "axes[0].tick_params(axis='both', labelsize=20)\n",
    "\n",
    "\n",
    "axes[1].set_ylabel(\"\")\n",
    "axes[1].tick_params(axis='both', labelsize=20)\n",
    "axes[1].set_ylabel(\"Turn-taking index\", fontsize=label_size)\n",
    "\n",
    "\n",
    "axes[0].set_xlabel(\"Round Index\", fontsize=label_size)\n",
    "axes[1].set_xlabel(\"Round Index\", fontsize=label_size)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.3, hspace=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true"
   },
   "source": [
    "# Figure S9: Solver ticks on tasks in Phases 1 and 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\mathregular{log_{10}}$(Solver time [ticks])')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "df_csop_tasks = pd.concat([pd.DataFrame(csop.task_specs['phase_1']),\n",
    "                           pd.DataFrame(csop.task_specs['phase_2'])], ignore_index=True).iloc[[1,3,5,7,8,2,4,9,10,11]].reset_index().rename(columns={\"usedIn\":\"Phase\"})\n",
    "\n",
    "df_csop_tasks['n'] = [len(x) for x in df_csop_tasks['students']]\n",
    "df_csop_tasks['m'] = [len(x) for x in df_csop_tasks['rooms']]\n",
    "df_csop_tasks['q'] = [len(x) for x in df_csop_tasks['constraints']]\n",
    "\n",
    "df_csop_tasks['nmq'] = df_csop_tasks.apply(lambda x: \"[{},{},{}]\".format(x.n, x.m, x.q), axis=1)\n",
    "df_csop_tasks[\"difficulty_num\"] = df_csop_tasks[\"difficulty\"].map({\"Easy\":1, \"Medium\":2, \"Hard\":3, \"Very Hard\":4, \"Super Hard\":5})\n",
    "df_csop_tasks = df_csop_tasks.sort_values([\"difficulty_num\", \"Phase\", \"computeTime\"]).reset_index(drop=True)\n",
    "\n",
    "df_csop_tasks.loc[[0,1,2,3], \"nmq\"] = df_csop_tasks.loc[[0,1,2,3], \"nmq\"] + [\"a\", \"b\", \"c\", \"d\"]\n",
    "df_csop_tasks.loc[[5,6,7], \"nmq\"] = df_csop_tasks.loc[[5,6,7], \"nmq\"] + [\"a\", \"b\", \"c\"]\n",
    "df_csop_tasks['Phase'] = df_csop_tasks['Phase'].map({\"step1\":\"Phase 1\", \"step2\":\"Phase 2\"})\n",
    "\n",
    "\n",
    "# df_csop_tasks = df_csop_tasks.query(\"Phase == 'Phase 2'\").reset_index().assign(complexity=csop2.COMPLEXITIES)\n",
    "\n",
    "\n",
    "\n",
    "sns.pointplot(x=df_csop_tasks['nmq'], \n",
    "              y=np.log10(df_csop_tasks['computeTime']), \n",
    "              linestyles=[\"\", \"\"], hue=df_csop_tasks[\"Phase\"], scale=2)\n",
    "\n",
    "# sns.pointplot(x=df_csop_tasks['complexity'], \n",
    "#               y=np.log10(df_csop_tasks['computeTime']), \n",
    "#               linestyles=[\"\", \"\"], hue=df_csop_tasks[\"Phase\"], scale=2, legend=None)\n",
    "\n",
    "\n",
    "\n",
    "plt.xticks(rotation=0)\n",
    "plt.axvline(x=3.5, color=\"black\", linestyle=\"--\")\n",
    "plt.axvline(x=4.5, color=\"black\", linestyle=\"--\")\n",
    "plt.axvline(x=7.5, color=\"black\", linestyle=\"--\")\n",
    "plt.axvline(x=8.5, color=\"black\", linestyle=\"--\")\n",
    "plt.text(1, 1.5, \"Very low\",  fontsize=20)\n",
    "plt.text(3.7, 1.5, \"Low\", fontsize=20)\n",
    "plt.text(5.5, 1.0, \"Moderate\", fontsize=20)\n",
    "plt.text(7.65, 1.5, \"High\", fontsize=20)\n",
    "plt.text(8.65, 1.5, \"Very\\nhigh\", fontsize=20)\n",
    "plt.xlabel(\"[N,M,Q]\", fontsize=23)\n",
    "plt.xticks(fontsize=20, rotation=60)\n",
    "plt.yticks(fontsize=20)\n",
    "\n",
    "plt.ylabel(\"$\\mathregular{log_{10}}$(Solver time [ticks])\", fontsize=23)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true"
   },
   "source": [
    "# Figure S10: Group composition vs collective performance, using all cognitive styles "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dfs = []\n",
    "\n",
    "for outcome in [\"score_zscore\", \"round_duration_zscore\", \"efficiency_zscore\"]:\n",
    "    for complexity in csop2.COMPLEXITIES:\n",
    "        model = smf.ols(\"{} ~ p1_score_mean_zscore + p1_score_std_zscore + rme_mean_zscore + cogstyle_diversity_q1 + cogstyle_diversity_q2 + cogstyle_diversity_q3\".format(outcome), \n",
    "                       data = df.query(\"complexity == '{}'\".format(complexity))).fit()\n",
    "\n",
    "        model_dfs.append(pd.DataFrame({'coef':model.params, 'se':model.bse, 'outcome':outcome, 'complexity':complexity}).reset_index().rename(columns={'index':'independent_variable'}))\n",
    "        \n",
    "    model = smf.mixedlm(\"{} ~ (p1_score_mean_zscore + p1_score_std_zscore + rme_mean_zscore + cogstyle_diversity_q1 + cogstyle_diversity_q2 + cogstyle_diversity_q3)\".format(outcome), \n",
    "                   data = df, groups=\"game_id\").fit()\n",
    "\n",
    "    model_dfs.append(pd.DataFrame({'coef':model.params, 'se':model.bse, 'outcome':outcome, 'complexity':\"Overall\"}).reset_index().rename(columns={'index':'independent_variable'}))\n",
    "    \n",
    "    \n",
    "    \n",
    "df_regressions = pd.concat(model_dfs,ignore_index=True)\n",
    "df_regressions['err'] = df_regressions['se']*1.96"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outcomes = [\"score_zscore\", \"round_duration_zscore\", \"efficiency_zscore\"]\n",
    "ivs = [\"cogstyle_diversity_q1[T.True]\", \"cogstyle_diversity_q2[T.True]\", \"cogstyle_diversity_q3[T.True]\"]\n",
    "\n",
    "fig, axes= csop2.composition_vs_perf_plots(df_regressions,\n",
    "                                          outcomes=outcomes,\n",
    "                                          variable_order=ivs, figsize=(13,10),\n",
    "                                          ind_var_labels=[\"Exploration\\nvs. satisficing\", \"Tolerance\\nof violations\", \"Optimizer vs.\\nresolver\"],\n",
    "                                          erroralpha=0.5)\n",
    "\n",
    "\n",
    "axes[0].set_xlabel(\"\")\n",
    "axes[1].set_xlabel(\"Coefficient\", fontsize=25)\n",
    "axes[2].set_xlabel(\"\")\n",
    "\n",
    "axes[0].set_title(\"Normalized score\", fontsize=25)\n",
    "axes[1].set_title(\"Duration\", fontsize=25)\n",
    "axes[2].set_title(\"Efficiency\", fontsize=25)\n",
    "\n",
    "axes[0].tick_params(axis=\"y\", rotation=30)\n",
    "\n",
    "\n",
    "plot_marker_alpha(axes, [0,1,2,3,4], 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true"
   },
   "source": [
    "# Figure S12: Out-of-sample permutation feature importance on full set of features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 196/196 [00:00<00:00, 427.98it/s]\n",
      " outer:   0%|          | 0/19 [00:00<?, ?it/s]\n",
      " inner loop:   0%|          | 0/30 [00:00<?, ?it/s]\u001b[A\n",
      " inner loop:   3%|▎         | 1/30 [00:00<00:04,  6.27it/s]\u001b[A\n",
      " inner loop:   7%|▋         | 2/30 [00:00<00:04,  6.32it/s]\u001b[A\n",
      " inner loop:  10%|█         | 3/30 [00:00<00:04,  6.29it/s]\u001b[A\n",
      " inner loop:  13%|█▎        | 4/30 [00:00<00:04,  6.29it/s]\u001b[A\n",
      " inner loop:  17%|█▋        | 5/30 [00:00<00:03,  6.32it/s]\u001b[A\n",
      " inner loop:  20%|██        | 6/30 [00:00<00:03,  6.25it/s]\u001b[A\n",
      " inner loop:  23%|██▎       | 7/30 [00:01<00:03,  6.26it/s]\u001b[A\n",
      " inner loop:  27%|██▋       | 8/30 [00:01<00:03,  6.26it/s]\u001b[A\n",
      " inner loop:  30%|███       | 9/30 [00:01<00:03,  6.19it/s]\u001b[A\n",
      " inner loop:  33%|███▎      | 10/30 [00:01<00:03,  6.23it/s]\u001b[A\n",
      " inner loop:  37%|███▋      | 11/30 [00:01<00:03,  6.20it/s]\u001b[A\n",
      " inner loop:  40%|████      | 12/30 [00:01<00:02,  6.24it/s]\u001b[A\n",
      " inner loop:  43%|████▎     | 13/30 [00:02<00:02,  6.25it/s]\u001b[A\n",
      " inner loop:  47%|████▋     | 14/30 [00:02<00:02,  6.28it/s]\u001b[A\n",
      " inner loop:  50%|█████     | 15/30 [00:02<00:02,  6.26it/s]\u001b[A\n",
      " inner loop:  53%|█████▎    | 16/30 [00:02<00:02,  6.28it/s]\u001b[A\n",
      " inner loop:  57%|█████▋    | 17/30 [00:02<00:02,  6.33it/s]\u001b[A\n",
      " inner loop:  60%|██████    | 18/30 [00:02<00:01,  6.27it/s]\u001b[A\n",
      " inner loop:  63%|██████▎   | 19/30 [00:03<00:01,  6.23it/s]\u001b[A\n",
      " inner loop:  67%|██████▋   | 20/30 [00:03<00:01,  6.24it/s]\u001b[A\n",
      " inner loop:  70%|███████   | 21/30 [00:03<00:01,  6.25it/s]\u001b[A\n",
      " inner loop:  73%|███████▎  | 22/30 [00:03<00:01,  6.29it/s]\u001b[A\n",
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     ]
    }
   ],
   "source": [
    "baseline_LR_full, df_featimp_LR_full = q2_feature_importance(df_featureimportance, LinearRegression(), full_features, \"p2_cumulative_normscore\", 30)\n",
    "baseline_RF_full, df_featimp_RF_full = q2_feature_importance(df_featureimportance, RandomForestRegressor(), full_features, \"p2_cumulative_normscore\", 30)\n",
    "baseline_EN_full, df_featimp_EN_full = q2_feature_importance(df_featureimportance, ElasticNet(), full_features, \"p2_cumulative_normscore\", 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_imp = 100 * (baseline_LR_full - df_featimp_LR_full)/baseline_LR_full\n",
    "feature_imp_vis_LR_full = pd.DataFrame([feature_imp.mean(axis=0),feature_imp.std(axis=0)]).T.rename(columns={0:\"mean_diff\", 1:\"diff_std\"}).sort_values(\"mean_diff\", ascending=True) \n",
    "\n",
    "feature_imp = 100 * (baseline_RF_full - df_featimp_RF_full)/baseline_RF_full\n",
    "feature_imp_vis_RF_full = pd.DataFrame([feature_imp.mean(axis=0),feature_imp.std(axis=0)]).T.rename(columns={0:\"mean_diff\", 1:\"diff_std\"}).sort_values(\"mean_diff\", ascending=True) \n",
    "\n",
    "feature_imp = 100 * (baseline_EN_full - df_featimp_EN_full)/baseline_EN_full\n",
    "feature_imp_vis_EN_full = pd.DataFrame([feature_imp.mean(axis=0),feature_imp.std(axis=0)]).T.rename(columns={0:\"mean_diff\", 1:\"diff_std\"}).sort_values(\"mean_diff\", ascending=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "for result_df in [feature_imp_vis_LR_full, feature_imp_vis_RF_full, feature_imp_vis_EN_full]:\n",
    "    result_df.index = result_df.index.map(dict(zip(full_features, \n",
    "                                                                 [\"# females\", \"# college\", \"Diversity in\\nexploration preference\", \"Diversity in\\ntolerating violations\", \"Diversity in\\nprefering optimization\",\n",
    "                                                                  \"Avg. age\", \"Age diversity\", \"Skill\", \"Skill diversity\", \"Social perceptiveness\", \"Social perc. diversity\",\n",
    "                                                                  \"Avg. nominal-real gap\", \"Avg solution pace\", \"Avg post-complete solns\", \"Behavioral cog. style diversity\",\n",
    "                                                                  \"P1 efficiency\", \"P1 efficiency diversity\", \"P1 duration\", \"P1 duration diversity\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,25))\n",
    "\n",
    "feature_imp_vis_RF_full = feature_imp_vis_RF_full.loc[feature_imp_vis_LR_full.index]\n",
    "feature_imp_vis_EN_full = feature_imp_vis_EN_full.loc[feature_imp_vis_LR_full.index]\n",
    "\n",
    "plt.errorbar(x=feature_imp_vis_LR_full[\"mean_diff\"], \n",
    "             y=feature_imp_vis_LR_full.index, \n",
    "             xerr=1.96*feature_imp_vis_LR_full[\"diff_std\"], linestyle=\"\", label=\"Linear regression\", linewidth=3)\n",
    "plt.scatter(x=feature_imp_vis_LR_full['mean_diff'], y=feature_imp_vis_LR_full.index, s=150)\n",
    "\n",
    "\n",
    "plt.errorbar(x=feature_imp_vis_RF_full[\"mean_diff\"], \n",
    "             y=np.arange(len(feature_imp_vis_RF_full.index)) - 0.2, \n",
    "             xerr=1.96*feature_imp_vis_RF_full[\"diff_std\"], linestyle=\"\", color=\"orange\", label=\"Random forest\", linewidth=3)\n",
    "plt.scatter(x=feature_imp_vis_RF_full['mean_diff'], y=np.arange(len(feature_imp_vis_RF_full.index)) - 0.2, s=150, color=\"orange\")\n",
    "\n",
    "\n",
    "plt.errorbar(x=feature_imp_vis_EN_full[\"mean_diff\"], \n",
    "             y=np.arange(len(feature_imp_vis_EN_full.index)) - 0.4, \n",
    "             xerr=1.96*feature_imp_vis_EN_full[\"diff_std\"], linestyle=\"\", color=\"red\", label=\"ElasticNet\", linewidth=3)\n",
    "plt.scatter(x=feature_imp_vis_EN_full['mean_diff'], y=np.arange(len(feature_imp_vis_EN_full.index)) - 0.4, s=150, color=\"red\")\n",
    "\n",
    "\n",
    "\n",
    "plt.axvline(x=0, linestyle=\"--\", color=\"black\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "# plt.xlim(-0.1, 0.6)\n",
    "plt.tick_params(labelsize=25)\n",
    "plt.xlabel(\"% decrease in $Q^2$\", fontsize=25)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py38",
   "language": "python",
   "name": "py38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
